{"$schema": "https://c3voc.de/schedule/schema.json", "generator": {"name": "pretalx", "version": "2026.3.0.dev0", "url": "https://pretalx.com"}, "schedule": {"url": "https://pretalx.com/sotm2026-osm-science/schedule/", "version": "0.3", "base_url": "https://pretalx.com", "conference": {"acronym": "sotm2026-osm-science", "title": "State of the Map 2026 \u2013 OSM Science", "start": "2026-08-28", "end": "2026-08-30", "daysCount": 3, "timeslot_duration": "00:05", "time_zone_name": "Europe/Paris", "colors": {"primary": "#004dd0"}, "rooms": [{"name": "Martinique", "slug": "5495-martinique", "guid": "605a1946-3f98-5284-8944-831c42132504", "description": "Amphi Cauchy (Carnot), https://www.openstreetmap.org/way/1452638610", "capacity": 238}], "tracks": [], "days": [{"index": 1, "date": "2026-08-28", "day_start": "2026-08-28T04:00:00+02:00", "day_end": "2026-08-29T03:59:00+02:00", "rooms": {}}, {"index": 2, "date": "2026-08-29", "day_start": "2026-08-29T04:00:00+02:00", "day_end": "2026-08-30T03:59:00+02:00", "rooms": {"Martinique": [{"guid": "a619bfbd-7c49-5bd6-a396-ce209a669448", "code": "PAF7YC", "id": 101949, "logo": null, "date": "2026-08-29T09:30:00+02:00", "start": "09:30", "end": "2026-08-29T10:05:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-101949-osm-science-2026-introduction", "url": "https://pretalx.com/sotm2026-osm-science/talk/PAF7YC/", "title": "OSM Science 2026: Introduction", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This talk will introduce the OSM Science/SotM Academic Track concept, review its history and past editions and will discuss the current state of OSM Science as reflected in the talk included within this track", "description": "OpenStreetMap (OSM) enters its third decade amid pronounced change and instability. Corporate involvement continues to evolve. Since it was last documented in 2019 [1], corporate mapping - instrumental in expanding OSM beyond its early European core [2] - has declined [3] as the Overture Maps Foundation has emerged as a parallel venue for producing OSM-derived map products. COVID-19 caused further disruption: between 2020 and 2023, only one global State of the Map was held in person; contributor engagement and co-editing changed [4]; and the project gained visibility, contributing to record activity in May 2020 [5]. Geopolitical conflicts have likewise exposed OSM's close ties to events on the ground. Politically motivated vandalism during the war in Gaza prompted new rate limits and revealed tensions among openness, governance, and resilience [6], while bombardment and shifting front lines may have reshaped building contributions in Ukraine [7]. Meanwhile, OSM continues to expand into new communities and domains [2], even as large language models and other AI systems consume its data and infrastructure at unprecedented scale, raising questions about attribution, reciprocity, and the sustainability of a volunteer commons [8].\nAcademic research usually proceeds more slowly than real-world events because funding, research programs, and publication take time. Conference presentations, however, can reveal emerging trends because they accommodate ongoing and unpublished work. Indeed, topics presented in State of the Map talks across all tracks appeared in journal publications an average of 2.8 years later [9]. The abstracts in OSM Science 2026 can therefore be read as a timely, if necessarily provisional, scientific response to a project being reshaped by new actors, crises, technologies, and uses.\nSeveral topics outlined above recur in this edition of OSM Science 2026. Rihani et al. compare events on the front line of the war in Ukraine with patterns of building edits [7]; Boettcher examines digital-governance problems arising from AI-driven extraction of OSM data [8]; and Grinberger et al. analyze the long-term effects of corporate mapping on OSM's data landscape in Southeast Asia [10].\nThese contributions sit alongside studies of 'classic' OSM Science themes. Despite the project's maturity, data quality continues to attract substantial attention. A substantial group of papers addresses it directly, while several related contributions combine quality assessment with tools or applications. Levin et al. propose an intrinsic completeness indicator that uses the geometric attributes of mapped entities to identify potentially incomplete regions [11]. Bourbonnais et al. quantify the errors introduced when POI centroids are used as proxies for actual entrances [12], while Tauscher and Enkhtamir estimate the amount of potentially mappable publicly accessible indoor space and provide a basis for calculating the completeness of indoor mapping [13]. Bolognani and Herfort conduct a relation-aware audit of the Dutch cycling network [14]. Flemming's Public Transport Stop Analysis project, which offers a technical contribution to enable rapid, planet-scale calculations, visualizes mapping conventions and flags inconsistent or potentially erroneous data [15]. Finally, Mazagol identifies meaningful gaps and classification errors in the mapping of mining activities relative to scientific and national inventories [16], while Sarretta et al. document sparse OSM coverage, very low adoption of dedicated tags, and missing condition and maintenance attributes for Alpine torrent-control structures, and propose steps toward a more complete interoperable inventory [17].\nThese studies focused on data are complemented by efforts to make OSM data and contributions more accessible and interpretable. Some focus on understanding edits: Bernard et al.'s GeoChange Ontology, processing pipeline, knowledge graph, and web application classify transitions to distinguish real-world change from correction, refinement, and modeling decisions [18], while Rieger et al.'s OSM Road Monitor compares road edits with masks derived from recent remote-sensing imagery to flag possible inaccuracies [19]. Other contributions take a governance-oriented approach. Liebel examines the Humanitarian OpenStreetMap Team (HOT) Tasking Manager as a sociotechnical interface that can both address and reproduce asymmetries in humanitarian mapping [20], while C\u00e1mara-Menoyo and Monteath analyze diversity and exclusion in OSM's formal tagging-proposals process [21]. Further studies support mapping directly. StreetMeasure combines semantic segmentation, monocular depth estimation, crowdsourced 360-degree imagery, and limited field measurements to recommend measurement tags [22]. Finally, two cartographic tools make OSM-derived outputs more accessible. Bourcier and Touya present the use of CartAGen, an open-source Python library containing more than 80 generalization algorithms and offering a QGIS processing interface, on OSM data with the goal of designing a high-quality pan-scalar map [23]. Amanuel studies the rapid uptake of Terraink, a service that converts OSM data into printable city posters without requiring GIS or programming expertise, using its release to examine non-expert demand and the infrastructural effects of increased consumption [24].\nDespite persistent data-quality issues and the continuing need for new tools, OSM remains widely used, as demonstrated by the many domain-specific studies in this edition 2026. Most concern transport and mobility: Rao proposes reconstructing high-resolution lane-level road data from OSM centerlines [25]; Szeliga et al. describe using OSM-derived variables to model operating speed through spatial regression [26]; Votz describes inferring applicable speed limits on an offline smartphone and comparing them with measured vehicle speed [27]; Klinkhardt et al. discuss deriving attractiveness scores for destination-choice models [28]; and Jeansoulin and Gambette deliver enrichment of  historical railway archives [29]. A second group of studies address welfare, health, and humanitarian concerns: Ihantamalala et al. model access to health services in rural Madagascar [30], Loizeau et al. assess the health potential of urban green spaces [31], and van der Heijden describes detecting slums at the individual-building level [32]. A final, unusual application proposed by Stru\u015b et al. combines OSM-based route planning and urban-context information with community mapping, guided soundwalks, and building-level color observations to create multisensory urban experiences [33].\nTaken together, these contributions span domain applications, data-quality assessment, analyses of contribution dynamics and editing behavior, the development of tools that support OSM contributions, and questions of governance and participation. These themes echo those identified by the Scientific Committee of the 2019 State of the Map Academic Track, the predecessor of OSM Science [34]. This apparent continuity is also visible across the full set of Academic Track and OSM Science abstracts. Using a large-language-model-based process with human oversight and intervention, we classified all 122 non-editorial abstracts published between 2019 and 2026, allowing each abstract to be assigned to more than one category. The results (see Figure 1) show that every category is meaningfully represented in almost every year, although several patterns emerge. Data quality is the most stable topic, consistently accounting for about one-fifth of contributions. The development of tools that support OSM, while showing more variability, has also remained relatively stable since 2021. By contrast, attention to contribution dynamics and editing behavior appears to have declined as studies of governance and participation have become more common (even if somewhat declining recently). Domain applications have also increased and now form a consistently prominent category.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9VQ9VC", "name": "Yair Grinberger", "avatar": "https://pretalx.com/media/avatars/3UBLLA_M9YOOLd.webp", "biography": "Dr. Yair Grinberger is a senior lecturer of geoinformatics at the Department of Spatial Science at the Hebrew University of Jerusalem. His main interests are Geographical Information Science, OpenStreetMap Science, geo-cultural analysis, urban agent-based modeling, and mobility analysis.", "public_name": "Yair Grinberger", "guid": "9a9c1f4c-554f-5a44-90dd-91222bc44914", "url": "https://pretalx.com/sotm2026-osm-science/speaker/9VQ9VC/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/PAF7YC/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/PAF7YC/", "attachments": [{"title": "Slides - PDF", "url": "/media/sotm2026-osm-science/submissions/PAF7YC/resources/OSM_S_M0qANhj.pdf", "type": "related"}]}, {"guid": "cd9f3cb2-8dfd-5e51-8b07-5841d3a3dade", "code": "X89Y8Y", "id": 95926, "logo": null, "date": "2026-08-29T10:05:00+02:00", "start": "10:05", "end": "2026-08-29T10:10:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-95926-mapping-informal-settlements-with-openstreetmap-data-integrating-urban-morphology-and-topology-for-slum-detection", "url": "https://pretalx.com/sotm2026-osm-science/talk/X89Y8Y/", "title": "Mapping Informal Settlements with OpenStreetMap Data: Integrating Urban Morphology and Topology for Slum Detection", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This study presents a reproducible methodology for detecting informal settlements using OpenStreetMap building footprint data. By integrating urban morphology and topology within a Python-based workflow and leveraging OSMnx for data extraction, the approach enables fine-scale spatial analysis in data-scarce environments. The method is tested across three case studies in the Global South, demonstrating how OpenStreetMap can support urban research, comparative analysis, and data-driven insights into informal settlements relevant to SDG 11.", "description": "Rapid urbanisation is transforming cities worldwide, particularly in the Global South, where urban growth often occurs faster than formal planning processes can respond. As a result, informal settlements have expanded significantly and now shelter a substantial portion of the urban population, leading to several problems within them, e.g., lack of tenure security, access to safe water and acceptable sanitation, and housing durability [1]. Despite their lack of formal recognition, these settlements are home to countless individuals and families, forming integral components of the urban landscape. Despite this fact, these areas frequently remain underrepresented in official datasets and planning documents. This lack of reliable spatial information poses a major obstacle for urban planners, researchers, and policymakers seeking to address the challenges associated with informal urbanisation and monitor progress toward Sustainable Development Goal 11 (SDG 11), which aims to ensure inclusive, safe, resilient, and sustainable cities. For all mentioned, qualitative and quantitative geographic data collection and analysis are crucial to a better understanding of such urban contexts.\nThe acquisition of the mentioned spatial data is essential for understanding the morphology and dynamics of informal settlements. However, traditional approaches such as census surveys or remote sensing often face limitations: On the one hand, with remote sensing, the detection of large areas of slums struggles to capture the fine-grained spatial structures that characterise informal neighbourhoods. On the other hand, while census data is obstructed by the reality that population census and household surveys may be difficult or even impossible to collect or access in some less-developed countries [2].  These limitations highlight the need for alternative data sources and methodologies capable of supporting detailed spatial analysis of informal settlements.\nInformal settlements often exhibit distinctive spatial characteristics, including high building density, irregular plot structures, and complex patterns of spatial connectivity. Two fields have been exploring settlements with such dynamic and organic characteristics: urban morphology and topology. Urban morphology is a field of study focusing on the analysis of urban form and processes of its formation and transformation [3]. While topology is not an in-depth explored field for urban and geospatial analysis, some research has shown it represents a good approach to the study of complex urban structures like slums, because of the analytical tools for identifying incipient urban development in informal neighbourhoods [4]. Therefore, this research proposed a methodology to detect informal settlements by integrating principles of urban morphology and topology using building footprint data derived from OpenStreetMap (OSM). Urban morphology metrics (UMMs) were calculated to describe characteristics such as building area, perimeter, compactness, tessellation area, and inter-building distances. Morphological tessellations generated out of building footprints were used as a proxy for parcel structures in areas where cadastral information is scarce or unavailable. In addition, spatial topology was analysed within the cases of study through a graph-based representation of building relationships generated using Delaunay Triangulation. This network structure enabled the calculation of connectivity-based Topology Metrics (TMs), including the Average Weighted distance between buildings. \nIn recent years, Volunteered Geographic Information (VGI) has emerged as a powerful alternative for documenting urban environments that are poorly represented in official datasets. Among many others, OSM is one of the most successful examples of VGI [5] and has become one of the most important global repositories of openly accessible geospatial data. In many cities of the Global South, this openly available data provides one of the few spatially detailed representations of informal settlements.  Despite the concerns about completeness and contributor bias, VGI data from OSM have been validated in several studies [6], showing comparable positional accuracy to authoritative datasets. In addition, its participatory approach ensures that the data is both current and reflective of the actual conditions on the ground. The global coverage of OSM data, its continuous updates, and participatory nature provide valuable data, especially in under-mapped areas like informal settlements. A key component of the workflow for this research was the usage of the Python library OSMnx, which enabled the automated extraction of building footprints and the structuring of OSM data within a Python environment. The integration of the data acquisition through this library supported a fully reproducible and accessible pipeline that further allowed the spatial analysis of the slums.\nThe mentioned methodology was applied to three informal settlements located in different regions of the Global South where detailed OSM building footprint data was available: Asia, Africa and Latin America. The selection of these regions also responded to their particular conditions as major hosts of informal settlements, as Asia gathers over half of the world\u2019s slum population, and some cities have reached worrying levels of inequality, as it happens in Latin America and Africa [7]. The training dataset was derived from the Korail neighbourhood in Dhaka, Bangladesh, one of the largest and most densely populated slums in South Asia. To evaluate the transferability of the methodology across different regions, the model was tested in two additional neighbourhoods: Katanga in Kampala, Uganda, and Ricardo Brugada in Asuncion, Paraguay. \nTwo classification strategies were implemented to distinguish slum buildings from non-slum buildings: a stepwise search and a random forest. The first approach used a rule-based threshold searching method capable of identifying the optimal metric threshold within the separated clusters associated with informal settlements. To evaluate how well the threshold separates the clusters into slums and non-slums, a silhouette score was performed. The second approach applied a Random Forest machine learning classifier trained using the morphological and topological metrics derived from the OSM building footprints data. For both methods mentioned, and as a prior step to classification, building footprints and neighbourhood polygons were spatially intersected to provide the labelled data for the ground truthing. This highlights the importance of VGI as both an input dataset and a reference layer for creating training labels.\nThe results suggested that with the RF classifier, the model has learned the specific characteristics of the training data, including noise and outliers, which implies overfitting. By comparison, even though the TS strategy is not an automatic method, and the algorithm computation time was longer, it is less sensitive to the finer aspects of the training data, which led to better generalizability. In terms of the implementation of urban morphology and topology, the results demonstrate that the morphological metrics could capture the spatial characteristics that are typical of informal settlements, such as: smaller building areas, higher building densities, and shorter inter-building distances. However, while the topology-based metrics provided additional insights on the connectivity within the slums, their contribution to the building classification accuracy was very modest, in some cases, even led to a slight decrease in the performance.  These results and the comparative analysis across case studies selected from different continents suggest that informal settlements do not exhibit a single universal morphological and topological signature. While Korail is characterised by tightly packed and highly connected building clusters, Ricardo Brugada and Katanga display a different spatial logic of informality, where small housing units are more dispersed. This diversity helps to explain the limited transferability of the slum mapping model.", "recording_license": "", "do_not_record": false, "persons": [{"code": "JUQBFR", "name": "Lucia Mabel van der Heijden", "avatar": "https://pretalx.com/media/avatars/WDB9UJ_zAtBVVc.webp", "biography": "A cartographer with roots in Paraguay and academic experience across Europe through the Erasmus Mundus Cartography MSc. Currently starting her professional journey as a cartographer in Germany, with a strong interest in open data and social and urban research. Passionate about maps, infographics, cities, and the stories they tell.", "public_name": "Lucia Mabel van der Heijden", "guid": "1effccbd-3316-5a23-a280-d383abd20bed", "url": "https://pretalx.com/sotm2026-osm-science/speaker/JUQBFR/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/X89Y8Y/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/X89Y8Y/", "attachments": [{"title": "Presentation SotM Lucia", "url": "/media/sotm2026-osm-science/submissions/X89Y8Y/resources/Prese_Dsad1I0.pdf", "type": "related"}]}, {"guid": "cfd3ad42-8e2a-54a3-a582-059d207b71d6", "code": "KFDLAR", "id": 96899, "logo": null, "date": "2026-08-29T10:10:00+02:00", "start": "10:10", "end": "2026-08-29T10:15:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96899-mygreenspace-relying-on-open-data-to-characterize-the-health-potential-of-urban-green-spaces", "url": "https://pretalx.com/sotm2026-osm-science/talk/KFDLAR/", "title": "MyGreenSpace\u202f: Relying on Open Data to Characterize the Health Potential of Urban Green Spaces", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "The presentation aims to demonstrate how the collaborative and open data routinely collected in OSM can serve as a basis for calculating indicators designed to assess the potential of a green space to promote the health of city dwellers, across its various dimensions (physical, social, mental and environmental health).", "description": "**Background**:  \n\nExposure to urban green spaces \u2013 whether in the form of views of green landscapes [1] or visits to urban parks and gardens \u2013 offers health benefits for residents across a range of dimensions, including physical, social, mental and environmental health [2]. Green spaces help combat sedentary lifestyles and loneliness. They provide spaces for rejuvenation and protection from the various social stresses and environmental risks that characterise our contemporary lifestyles. They are one of the key drivers in adapting urban environments to the consequences of climate change, and to the frequency and intensity of heatwaves and heavy rainfall. \n\nThe theoretical framework for the health benefits of urban green spaces [3-7] summarises this capacity of green spaces to address the multiple health challenges facing populations through four functions: (1) mitigating exposure to environmental risk factors, (2) developing individual capacities, (3) providing a place for recharging, and (4) preserving and developing biodiversity. These functions are developed to varying degrees depending on the characteristics of the green space, and its health potential varies accordingly. Similarly, at the neighbourhood or city level, the types of green spaces, the density or regularity of their network, and their interconnection determine the health benefits that residents can derive from their presence in their residential or working environment. The integration of health considerations into policies and practices relating to the planning, development and management of urban green spaces therefore appears to be a public health issue that local authorities must take into account.  \n\nIn order to drive and promote the development of green spaces that promote health, we have taken the initiative to develop a tool for assessing the health potential of green spaces (PSEV) for local authorities. Although numerous resources exist to support local authorities in managing their parks and gardens, to our knowledge, none of these tools adopt a public health perspective or consider the multifunctionality of green spaces. Designed for use at national (French) or European level, this tool is being developed as part of the Greencan research project (2022\u20132026), funded by the National Cancer Institute and the Joint-Action Prevent Non-Communicable Diseases research-action initiative (2024\u20132027), supported by the European Commission.   \n\n**Method**: \n\nThe first phase of developing the tool for assessing the health potential of green spaces was the result of a collaborative effort between a public health research team and a working group comprising various local stakeholders from the fields of the environment, planning and urban development, as well as officials from the City and Metropolitan Area of Rennes (Brittany, France). The aim was to identify indicators of the health potential of green spaces that would enable the level of development of each of their functions to be estimated. These indicators are based on the design characteristics of green spaces, for example their vegetation components (the presence of deciduous trees, lawns, gardens, etc.) or their various facilities, such as playgrounds, water points and toilets, etc. \n\nThe second phase involved identifying available and accessible data to inform the indicators of the health potential of green spaces in each European municipality, regardless of its size. With the support of a geomatics engineer and developer, we then developed a tool (a prototype) that enables both the collection of data necessary for calculating the indicators and estimating the health functions of green spaces, and the mapping of the health potential of a city\u2019s green spaces at the scale of geographical areas of varying sizes. \n\n**Results**: \n\nThe result is the development of a web platform called MyGreenSpace or mygreenspace.net, available in French, English and the languages of the European partner cities. Set up for Rennes (France), Rybnik (Poland), Turin (Italy) and Stockholm (Sweden) as part of the Joint Action PreventNCD project, MyGreenSpace is based on the OpenStreetMap (OSM) geolocation database. This was chosen due to its ability to record a wide range of features of green spaces (pathways, dog parks, etc.) useful for characterising their health potential, according to a common classification across the whole of Europe. For the four cities mentioned, MyGreenSpace extracts green spaces from OSM identified using the tags leisure=park and landuse=recreation_ground, combined with a \u2018name\u2019 filter.   \n\nFor each green space recorded in the MyGreenSpace database, two methods of data collection are used to provide information for the 31 indicators of the green space\u2019s health potential. Depending on the type of indicator \u2013 \u2018quantitative\u2019 or \u2018qualitative\u2019, i.e. relying on measurable or observable data \u2013 the data is either extracted directly and automatically from OSM, for the five quantitative indicators for example (leaf density, soil permeability, etc.); or the data comes from the form designed to provide information for the 15 strictly qualitative indicators (vegetation strata, sounds of nature, etc.). The remaining 11 indicators are described as \u2018mixed\u2019, in that they can be calculated using OSM data if available, or by completing the form. \n\nA graphical user interface has also been developed using the open-source software GoGoCarto. It enables users to describe the health benefits of each green space recorded in MyGreenSpace, to map the health benefits of green spaces within an urban area, and to contribute to data collection via the form or through OSM, as the OSM and MyGreenSpace databases are connected and synchronised. \n\n**Discussion-Conclusion**: \n\nThe MyGreenSpace prototype is designed for local authorities with the aim of promoting the development of green spaces that promote good health. It will be tested by the local authorities involved in Joint Action PreventNCD by September 2026. In Rennes, for example, staff from the Parks and Biodiversity Department will be responsible for filling in the missing data for green spaces in two neighbourhoods with very contrasting types of green spaces. This test will enable us to assess the choice of OSM as the reference database for MyGreenSpace and the estimation of the health potential of green spaces. We will pay close attention to how the green space categories proposed by OSM can be adapted to the needs of a tool for public health promotion and research. We will observe how municipal departments utilise OSM to complete data relating to their green spaces or make available the data they produce at a local level. We will explore the possibilities of enriching the OSM database with new data derived from qualitative indicators.", "recording_license": "", "do_not_record": false, "persons": [{"code": "NXNYTZ", "name": "Marion Porcherie", "avatar": null, "biography": "Marion Porcherie holds a PhD in Political Science and is an assistant professor at the French School of Public Health (EHESP) in Rennes, affiliated with the Ar\u00e8nes research unit (CNRS UMR 6051). Her research focuses on health promotion, the social and territorial determinants of health, and the integration of health into public policies, particularly in urban contexts.", "public_name": "Marion Porcherie", "guid": "ef2abc61-b72d-5391-8742-a613c70d1e9c", "url": "https://pretalx.com/sotm2026-osm-science/speaker/NXNYTZ/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/KFDLAR/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/KFDLAR/", "attachments": [{"title": "Lightning talk Marion Porcherie", "url": "/media/sotm2026-osm-science/submissions/KFDLAR/resources/OSM_S_FDzGF7A.pdf", "type": "related"}]}, {"guid": "8ec09920-4330-5d96-8fb3-ac6fa563d317", "code": "CTNPQJ", "id": 97025, "logo": null, "date": "2026-08-29T10:15:00+02:00", "start": "10:15", "end": "2026-08-29T10:20:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-97025-indoor-mapping-potential-in-openstreetmap-estimating-the-amount-of-publicly-accessible-indoor-spaces-across-european-cities", "url": "https://pretalx.com/sotm2026-osm-science/talk/CTNPQJ/", "title": "Indoor Mapping Potential in OpenStreetMap: Estimating the Amount of Publicly Accessible Indoor Spaces Across European Cities", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "With this study we aim to quantify the amount of mappable indoor space in European cities. On this base we can assess the completeness of indoor data in OSM. We operate under the premise that only publicly accessible spaces are relevant for inclusion in the OSM database. Thus, we first identify candidate buildings based on building tag values and spatially related amenities. We then employ values derived from Neufert design guidelines to estimate the portion that is actually publicly accessible. The method is tested with four European cities, Pecs, Dresden, Budapest and Berlin, and verified at random with selected buildings.", "description": "For details, see the paper in the proceedings: https://doi.org/10.5281/zenodo.21342252\n\nThe geodata in OpenStreetMap (OSM) is mainly collected and used for the purpose of providing orientation and guidance in public outdoor spaces in both urban and rural areas. To this end, dedicated geospatial applications such as cartographic maps and routing applications consume the OSM data. Many indoor spaces within buildings form closed islands within the public space and are not easily accessible and thus not interesting for OSM. In other places however, public space extends into buildings either through open entries or because spaces are only partially enclosed by built structures, forming a gradual passage from outdoor to indoor with half-open areas. Recently, maps and routers are increasingly extended to also cover publicly accessible indoor spaces, similarly to outdoor spaces. Subsequently, the data gathered needs to extend to the indoor and include the interior structure of buildings, rooms and walkable areas, paths through corridors and staircases. There has been a niche interest in indoor mapping for a long time already.  Respective work dates back to the early 2010s, with proposals for tagging schemas from the academic field (Goetz and Zipf 2011) as well as the mapping community (Poole et al. 2014). Recently, there is also a growing interest to use data from architectural design, construction processes and real estate management as a base of indoor mapping (e.g.  Krishnakumar, Tauscher, and Heigener 2023).\n\nWhile outdoor and indoor spaces are not fundamentally different, and hence mapping and data are similar, there are notable differences.  Indoor spaces have a more detailed structure compared to natural environments or infrastructure features, but similar to urban outdoor spaces designed for humans to stay in. Further, buildings, containing indoor spaces, consist of stacked walkable areas in multiple levels - similar as grade-separated street and rail infrastructure but employed to a greater extent with usually more than two levels on top of each other. All indoor relations, areas, ways as well as nodes for POIs are attached to these levels.\n\nCurrently, the indoor data is sparse in OSM with usually only few prominent buildings in a city mapped through dedicated mapping campaigns or coordinated efforts in research projects. According to taginfo.openstreetmap.org, there are over 700~million objects tagged as buildings, but only 1~million indoor objects and a typical building contains various indoor objects. However, to unlock the opportunities and potentials of new applications for integrated indoor-outdoor visualization, navigation and analysis, we would need an extensive survey and subsequent insertion of data about all kinds of indoor spaces into the OSM database. Given the more detailed nature of this data with intricate representation of interior structures and the fact of stacked areas multiplying the amount of data within a given footprint area, we assume that the increase of data in the database would be substantial and exert a notable effect on the OSM infrastructure, the underlying databases and network traffic. To judge these effects, it would be interesting to know which volume of indoor data would be created if all possible indoor spaces were comprehensively mapped in OSM. There is - to the best of our knowledge - no study yet that tries to quantify the potential amount of indoor data to be mapped in OSM. With this study we aim to estimate this potential and as a side result we can make a statement about the completeness of indoor data, similar to earlier studies that evaluate quality and completeness of building data (Biljecki, Chow, and Lee 2023).\n\nWe operate under the premise that only publicly accessible spaces are relevant for inclusion in the OSM database. At the same time, only very few buildings are comprised entirely of public spaces. Thus we must not only identify buildings that have a public function, but also estimate the portion of spaces within those buildings that are actually publicly accessible. We base our estimation of \"mappable\" indoor spaces on an areal (square) measure, hence footprint areas of buildings, levels and rooms or open spaces in buildings. First, we identify various classes of buildings that are publicly accessible, at least partially, such as stations, malls, universities, hospitals, town halls etc. Second, we apply a heuristic method to estimate the percentage of publicly accessible areas for these classes of buildings. In a third step, we extract the relevant building data for a limited geographical scope, calculate the total building area within that scope across all buildings with all their levels. Finally, by applying the percentages from the second step, we calculate the estimated indoor mapping potential. We can then compare this value with the actual indoor data in OSM aggregated across the chosen geographical scope or building by building to retrieve some key figure of completeness.\n\n... full paper in proceedings:  https://doi.org/10.5281/zenodo.21342252\n\nBiljecki, Filip, Yoong Shin Chow, and Kay Lee. 2023. \"Quality of Crowdsourced Geospatial Building Information: A Global Assessment of OpenStreetMap Attributes.\" Building and Environment 237: 110295.  https://doi.org/10.1016/j.buildenv.2023.110295.\n\nGoetz, Markus, and Alexander Zipf. 2011. \"Extending OpenStreetMap to Indoor Environments: Bringing Volunteered Geographic Information to the Next Level.\" In Urban and Regional Data Management, edited by Sisi Zlatanova, Hugo Ledoux, and Elfriede Fendel, 51\u201362.  https://doi.org/10.1201/b11647-7.\n\nKrishnakumar, Subhashini, Helga Tauscher, and Dominik Heigener. 2023.  \"Floor Plan Extraction from Digital Building Models.\" In Proceedings of FOSSGIS (Academic Track) 2023, 146\u201352. Berlin, Germany.  https://doi.org/10.5281/zenodo.7576205.\n\nPoole, Simon, Tobias Knerr, Peda, and Andreas Hubel. 2014. \"Simple Indoor Tagging.\" https://wiki.openstreetmap.org/wiki/Simple_Indoor_Tagging.", "recording_license": "", "do_not_record": false, "persons": [{"code": "VKT3CE", "name": "Helga Tauscher", "avatar": null, "biography": "Helga Tauscher holds a professorship for computing in engineering at HTW Berlin. Her research interests revolve around graph-based model integration for data in the AEC (architecture, engineering and construction) industry, domain-specific languages and opensource software development. Previously, she has studied architecture at Dresden University of Applied Science, as well as at wei\u00dfensee academy of art Berlin and received her PhD in construction informatics from TU Dresden. She worked as a CAD/CAFM-expert in the elevator industry, as software developer for internet-based construction project management, as a researcher, lecturer and substitute professor in Kaiserslautern, Singapore and Weimar.", "public_name": "Helga Tauscher", "guid": "d2057da6-b8e8-5b4d-9b63-12e6902a986c", "url": "https://pretalx.com/sotm2026-osm-science/speaker/VKT3CE/"}, {"code": "JYT8X3", "name": "Nomin Enkhtamir", "avatar": null, "biography": "Nomin Enkhtamir is a PhD candidate in urban geography at the University of P\u00e9cs, holding a BSc in civil and structural engineering and an MA in landscape architecture from MATE Budapest. Nomin spent around four years working in reinforced concrete structural engineering, alongside landscape and public space projects such as a participatory playground design and a thesis on public park design in Ulaanbaatar. This mix of engineering, landscape design, and mapping shapes Nomin\u2019s interest in how cities and their indoor spaces can be represented in spatial data.", "public_name": "Nomin Enkhtamir", "guid": "373d9dcf-4046-5cfd-b297-1ec3299b1233", "url": "https://pretalx.com/sotm2026-osm-science/speaker/JYT8X3/"}], "links": [{"title": "Paper in proceedings", "url": "https://doi.org/10.5281/zenodo.21342252", "type": "related"}], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/CTNPQJ/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/CTNPQJ/", "attachments": []}, {"guid": "fdf004ad-3d66-5d5d-9ac5-0c27270b5f89", "code": "TDMGNZ", "id": 98130, "logo": null, "date": "2026-08-29T10:20:00+02:00", "start": "10:20", "end": "2026-08-29T10:25:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-98130-towards-an-openstreetmap-based-open-database-of-transversal-torrent-control-structures-in-the-alpine-arc", "url": "https://pretalx.com/sotm2026-osm-science/talk/TDMGNZ/", "title": "Towards an OpenStreetMap-based open database of transversal torrent control structures in the Alpine arc", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This work examines OpenStreetMap as an open interoperability layer for Alpine torrent control structures (e.g., check dams, bed sills), aligning the waterway=check_dam tagging schema with published hydro-geomorphic risk indices (e.g., PFI, MPi, SCR). A snapshot across European countries reveals very low schema adoption and attribute completeness; a conflation pilot against the regional database of the Friuli-Venezia Giulia Region (Northeastern Italy) establishes the first OSM-to-cadastre benchmark in this domain.", "description": "**Introduction and background**\nTransversal torrent control structures (TTCSs) \u2014 check dams, bed sills, consolidation and debris-retention works \u2014 have been built across Alpine mountain streams for more than a century to mitigate hydro-geomorphic risk. The need for systematic regional-scale assessment has grown with the increasing frequency of extreme precipitation events and the progressive ageing of infrastructure built decades ago. Research groups working in the Italian Alps  proposed  some  multi-parameter indices that need up-to-date information on the TTCSs: the Potential Fragility Index (PFI) [1], tested on 3,556 structures in Friuli-Venezia Giulia Region (FVG) (Northeastern Italy); the Maintenance Priority Index (MPi) [2], derived from multi-temporal high-resolution topography and the FVG regional database; and the Sediment Continuity Ratio (SCR) [3], which quantifies the interaction of TTCSs with sediment cascades. These indices share a common data dependency: a complete, attribute-rich, geolocated inventory of structures, including intrinsic variables (year of construction, height, material, typology) and extrinsic context (channel reach classification, process domain, lithological setting).\nSuch inventories exist but are fragmented and not constantly updated. The FVG database published through IRDAT [4] is exemplary, open-licensed under IODL 2.0 and served via OGC WMS/WFS. Equivalent database across the Alpine arc (e.g., Italian regions, Austrian WLV, Swiss cantons, French RTM, Slovenian DRSV) vary widely in accessibility, completeness, and interoperability. No pan-Alpine, openly licensed, interoperable TTCS inventory currently exists. This work targets the OpenStreetMap (OSM) as a complementary interoperability layer that any regional inventory could conflate against.\nThe `waterway=check_dam` tagging proposal ([5], initiated 2021, still pending) offers a structurally rich classification of check-dam types and materials, but is semantically poor with respect to PFI/MPi/SCR variables and does not cover the full TTCS typological range (e.g., bed sills fall outside its scope). Prior OSM-science work on authoritative data integration ([6], [7]) and citizen-science inventories for natural hazards ([8]) provides methodological grounding, but the TTCS domain has not been addressed.\n\n**Aim**\nThis work is organised around two connected questions. The first concerns the OSM schema: can the existing `waterway=check_dam` proposal (and a companion schema for grade-protection sills) be extended to carry the variables required by published hydro-geomorphic indices, compatibly with established tagging conventions? The second concerns OSM's empirical coverage: i) how complete, positionally accurate and attribute-rich is the existing TTCS representation when benchmarked against an authoritative regional  database; ii) what does the gap suggest about the potential for a safety-aware, distributed contribution model using OSM as a shared layer between authoritative institutional sources and citizen field observation, transferable across the Alpine arc?\n\n**Methodology and early results**\nThe schema crosswalk maps each input variable of the PFI, MPi and SCR indexes to existing or newly proposed OSM keys. Intrinsic variables largely align with existing conventions: typology (`check_dam=*`), material (`material=*`), height (`height=*`), construction year (`start_date=*`), operator (`operator=*`), and condition (`condition=*`). Extrinsic variables would require new sub-keys modelled as an authoritative overlay rather than community-observable attributes (e.g. `check_dam:reach=*`, `check_dam:process_domain=*`, `check_dam:lithology=*`), as they depend on geomorphological expertise and external authoritative datasets. The crosswalk also identifies the need for a companion sibling proposal, `waterway=bed_sill`, for grade-control sills, functionally distinct from check dams in function, geometry, and failure modes, framed as community contributions to the OSM tagging process, not as parallel schemas.\nBuilding on this crosswalk, a conflation pilot (to the authors' knowledge, the first OSM-to-cadastre comparison for TTCSs) will compare the IRDAT FVG database with OSM coverage in a test catchment from the Chiarel et al. (2026) study area. The closest methodological precedent is the OSM conflation of the US National Inventory of Dams [9], which targeted large dams with no fragility attributes; this pilot extends the pattern to torrential structures and PFI/MPi attribute coverage.\nA snapshot analysis of the OSM planet (2026-04-22, Geofabrik extract) quantifies the current gap. Italy carries only 17 `waterway=check_dam` features against 12,881 `waterway=weir`, a schema-adoption ratio of 0.11 %. Across European countries, the ratio reaches 0.56 %, driven almost entirely by Switzerland (652 features, 82 % of the European total). Changeset analysis reveals this concentration stems mainly from a single mapper working from national imagery (SwissImage / Swisstopo), not a coordinated process or import. Accordingly, none of the PFI/MPi input variables (`start_date`, `condition`, `material`, `height`) exceeds 1 % completeness Europe-wide: mass without quality.\nA contributing factor is the status of the `waterway=check_dam` proposal itself: initiated in 2021, formally pending for five years. Without an approved tag, both iD and JOSM exclude it from default presets, leaving the schema invisible to most mappers who rely on preset-guided workflows and reinforcing the low-adoption condition that keeps the proposal stalled.\n\n**Discussion**\nThe empirical results reveal a gap that is not merely quantitative but structural. The TTCS domain is intrinsically specialist: structures sit in remote, often hazardous catchments; relevant attributes require geomorphological expertise; and post-import verification \u2014 routine for urban features \u2014 demands physical access and domain knowledge that cannot be expected of the general OSM community. These characteristics explain why, even where authoritative database are openly licensed, the path to OSM import has not been activated: ODbL compatibility must be verified, a technically sound import process must be designed, and a community with sufficient competence must commit to maintaining the result.\nThe response to this structural gap should not be to lower expectations but to design for it explicitly. Methodologically, this work provides the first systematic alignment of an OSM tagging schema with a validated family of hydro-geomorphic risk indices for torrent control structures and proposes the first OSM-to-database conflation study in this domain; a future work on a companion `waterway=bed_sill` proposal would complete the typological coverage.\nPractically, this work sketches an activation pathway whose feasibility remains to be verified. On the OSM side, this involves reopening the `waterway=check_dam` proposal and exploring whether preset-backed workflows could lower the participation barrier. On the institutional side, it requires early dialogue with agencies holding authoritative TTCS inventories (e.g., IRDAT FVG, WLV Austria, French BD-RTM) to assess ODbL-compatible release and structured import. Grounding this pathway in peer-reviewed, validated indices may help position OSM as a legitimate interoperability layer for hazard-domain applications, potentially enabling regional-scale PFI/MPi/SCR screening across the Alpine arc.\nSeveral directions remain open: i) identifying which TTCSs can be safely documented is a prerequisite for any participatory contribution model and itself an open research problem requiring input from territorial authorities and domain experts; ii) how to structure long-term participation is equally unclear: the model would need to emerge from community dialogue, not be designed in advance; iii) extension to additional Alpine regions and monitoring of schema adoption are further directions this work can only point to.\nAll artefacts (e.g., crosswalk tables, conflation notebooks, analysis pipeline) will be released under open licences and based on open-source code to support reproducibility.", "recording_license": "", "do_not_record": false, "persons": [{"code": "M7P3RM", "name": "Alessandro Sarretta", "avatar": null, "biography": "Alessandro Sarretta is a researcher at the Italian National Research Council (CNR), since 2019 in the Research Institute for Geo-hydrological Protection, in Padua, previously at the Institute of Marine Sciences, in Venice. He deals, in marine/coastal and now geomorphological fields, with environmental data management and processing, Spatial Data Infrastructures, implementation of Decision Support Systems, standards and interoperability of research data. He is interested and involved in various fields of \"openness\", from open source software to open science, open knowledge and participatory mapping.", "public_name": "Alessandro Sarretta", "guid": "c30b2b3e-bfec-586c-9ba5-d4359d2fa92f", "url": "https://pretalx.com/sotm2026-osm-science/speaker/M7P3RM/"}], "links": [{"title": "Conference paper", "url": "https://doi.org/10.5281/zenodo.21607102", "type": "related"}, {"title": "Presentation", "url": "https://doi.org/10.5281/zenodo.22150818", "type": "related"}], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/TDMGNZ/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/TDMGNZ/", "attachments": []}, {"guid": "c7ce58e3-2173-5489-a080-ce929168c1a9", "code": "EUWM8V", "id": 96860, "logo": null, "date": "2026-08-29T10:25:00+02:00", "start": "10:25", "end": "2026-08-29T10:30:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96860-mapping-the-city-in-the-rhythm-of-colours-and-sounds-openstreetmap-as-a-basis-for-multisensory-civic-cartography", "url": "https://pretalx.com/sotm2026-osm-science/talk/EUWM8V/", "title": "Mapping the City in the Rhythm of Colours and Sounds: OpenStreetMap as a Basis for Multisensory Civic Cartography", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This paper presents City in the Rhythm of Colours and Sounds, a project combining OpenStreetMap, soundscape studies and Colouring Cities-inspired building data. Using selected Polish towns as case studies, it links OSM-based spatial layers with field soundwalks and participatory observation. It shows how open mapping can support multisensory civic cartography, urban education and social engagement. The project highlights OpenStreetMap as both a technical geodatabase and a community platform for understanding everyday urban experience.", "description": "This paper presents the concept and methodological framework of the rector\u2019s grant project City in the Rhythm of Colours and Sounds, which explores how OpenStreetMap can support multisensory, socially engaged and educational forms of urban mapping. The project combines three fields that are usually developed separately: community mapping, soundscape studies and building-level urban data inspired by the Colouring Cities approach. Its main aim is to show that OpenStreetMap may serve not only as a source of spatial reference data, but also as a practical civic platform around which different users can observe, describe, discuss and better understand the everyday urban environment.\nThe project is grounded in the idea of volunteered geographic information, understood as a shift from citizens being only consumers of geographic information to becoming active producers of spatial knowledge [1]. OpenStreetMap is one of the most important examples of this process, as it combines local observation, community editing and open geographic data into a continuously developed map of the world [2]. At the same time, the project recognises that OSM data should be used critically. Earlier studies have shown both the high potential and the spatial variability of OSM quality, especially when its data are used for analytical purposes rather than simple cartographic display [3]. Recent research on crowdsourced building information also indicates that OpenStreetMap can be highly valuable for urban studies, but that the completeness and consistency of building attributes vary between countries, cities and thematic categories [4]. This makes OSM particularly suitable for an educational project in which participants learn not only how to use open data, but also how to evaluate, verify and improve them.\nThe project focuses on selected small and medium-sized towns in Poland, including Tuch\u00f3w, Krosno, Pabianice, Andrych\u00f3w and Szczyrk. These towns represent different urban contexts: historic centres, post-industrial districts, residential estates, transport corridors and tourist landscapes. Such diversity allows us to compare how the built environment, street network, land use and public space structure influence the sensory experience of the city. Instead of treating the city only as a visual or functional system, the project proposes to study it as a lived environment composed of colours, materials, sounds, rhythms, movements and social practices.\nOpenStreetMap plays several roles in this framework. First, it provides the spatial base for fieldwork: streets, paths, building footprints, land use, amenities, green areas, public facilities and transport infrastructure. These data help plan soundwalk routes, select observation points and define local urban contexts around each point. Second, OSM supports the interpretation of the soundscape by allowing researchers and participants to relate acoustic observations to mappable urban features, such as road hierarchy, building density, service concentration, green space distribution or proximity to railway lines and industrial areas. Third, the project creates an opportunity for social OSM activity. Participants can verify existing map data, identify missing elements and improve the representation of local public spaces. The intention is not to overload OpenStreetMap with subjective or temporary sound impressions, but to use the project to strengthen verifiable mapping: paths, benches, crossings, public facilities, green areas, land-use details, building functions and other features relevant to everyday urban experience.\nThe methodological design consists of two connected parts. The first part concerns the preparation of an urban sound route and a building-based \u201ccolour map\u201d inspired by Colouring Cities. The Colouring Cities approach has been developed as an open platform for collecting, verifying and visualising building-level urban data, including information on building age, use, type, structure and sustainability-related characteristics [5], [6]. In this project, building footprints and selected urban attributes are prepared using OpenStreetMap and complementary open data where necessary. Attributes may include building function, approximate age, height, use, relationship to public space and surrounding land use. This creates a spatial background for reading the city as a material and functional environment.\nThe second part involves guided soundwalks with different groups of participants, such as students, residents or local stakeholders. During these walks, participants document the soundscape at selected points, noting dominant sound sources, perceived intensity, time of day, weather conditions, spatial context and their own perception of comfort or disturbance. This approach refers to soundscape research in which the acoustic environment is understood not only as a measurable physical phenomenon, but also as something perceived, interpreted and evaluated by people in context [7]. Previous studies have shown that soundscape perception can be described through dimensions such as pleasantness, eventfulness, calmness or annoyance, and that these dimensions can be linked with the spatial character of urban environments [8], [9]. Soundwalking, in turn, is treated as an empirical and participatory method for identifying components of the soundscape in specific places [10]. The collected observations are then linked with the surrounding mapped environment, for example through buffers around soundwalk points or comparison between historic centres, housing estates, industrial areas and tourist zones.\nA central element of the project is multisensory cartography. The map is treated not only as a technical product, but also as a medium for translating embodied urban experience into shared knowledge. Sound is mapped together with colour, function and morphology. This makes it possible to ask questions that are difficult to answer using conventional urban maps alone. Do older town centres sound different from newer residential estates? How does traffic structure influence perceived acoustic comfort? Are tourist spaces dominated by seasonal and human sounds, while industrial areas produce more mechanical or background noise? Can building form, street width or the presence of greenery help explain differences in sound perception? By connecting soundscape data with OSM-based urban layers, the project aims to develop a clear and replicable method for analysing the sensory character of small and medium-sized towns.\nThe proposed approach has a strong social dimension. OpenStreetMap is understood here as a form of civic infrastructure: open, editable, locally grounded and understandable to non-specialists. The project uses this potential to encourage residents and students to become active observers of their environment. Participants are not only asked to consume maps, but also to question them, verify them and contribute to better local spatial knowledge. In this sense, mapping becomes a form of public education. It helps users notice what is often ignored: acoustic comfort, accessibility, the relation between buildings and streets, the quality of public space, the presence or absence of greenery, and the everyday sensory atmosphere of the city.\nThe expected outcomes include soundscape maps, OSM-based urban context layers, comparative profiles of selected towns, educational materials and an interactive presentation of results. For the State of the Map community, the project offers a case study of how OpenStreetMap can support local civic research without losing its core principles of openness, verifiability and community value. It argues that the future of community mapping can be not only more detailed, but also more attentive to how people actually experience urban space: through movement, sound, colour, memory and everyday use.", "recording_license": "", "do_not_record": false, "persons": [{"code": "F8U3ZS", "name": "Pawe\u0142 Stru\u015b", "avatar": null, "biography": "Pawe\u0142 Stru\u015b is a geographer based in Krak\u00f3w, Poland, affiliated with the University of the National Education Commission in Krak\u00f3w. His academic interests focus on urban geography, geoinformation, cartography, OpenStreetMap, social mapping and the use of spatial data in the analysis of everyday urban environments. He is particularly interested in participatory and educational applications of GIS and open geodata.", "public_name": "Pawe\u0142 Stru\u015b", "guid": "3151f9c8-f6cc-59a3-9700-4a95aa597921", "url": "https://pretalx.com/sotm2026-osm-science/speaker/F8U3ZS/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/EUWM8V/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/EUWM8V/", "attachments": []}, {"guid": "2cf0b46d-9cad-53c4-9182-5c9a84897c73", "code": "V9ASNX", "id": 96755, "logo": null, "date": "2026-08-29T11:15:00+02:00", "start": "11:15", "end": "2026-08-29T11:50:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-96755-cartagen-an-innovative-tool-to-generalise-osm-data", "url": "https://pretalx.com/sotm2026-osm-science/talk/V9ASNX/", "title": "CartAGen, an innovative tool to generalise OSM data", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "CartAGen is an open source Python library that highly facilitates the use of map generalisation algorithms. This new tool could change how basemap producers that rely on OSM data design their maps.", "description": "As OSM has become more consistent in terms of completeness, OSM data has become the foundation for many applications. Among these uses is the creation of base maps. In this article, by \u201cbase map\u201d we mean both topographic maps (i.e., those where topographic features are the main focus of the map) and base maps that accompany thematic data, even though this distinction may sometimes seem artificial (are commercial points of interest on Google Maps part of the map\u2019s topographic features, or are they thematic data surrounded by a base map that allows them to be located?). Indeed, we can cite several projects based on OSM data that involve the distribution of a pan-scalar base map through various geographic services (Figure 1): \n- (a) Mapbox: a vector tiles base map with the ability to customize its style via the Mapbox Studio application.\n- (b) CARTO: known for its Voyager, Positron, and Darkmatter base maps, often used to display thematic data due to their minimalist appearance.\n- (c) Stamen: known for its artistic base maps such as Toner (black and white) and Watercolor.\n\nRegardless of the quality of these products, they all share a minimal use of cartographic generalization techniques. Two main reasons can be cited to explain this assertion. First, with the development of multi-scale maps, it was believed that the ability to navigate between scales using the zoom and pan functions offered by these maps would eliminate the primary reason why generalization is necessary [1]. Indeed, when designing a paper map covering a vast area\u2014and thus at a single scale\u2014generalization becomes essential to reduce the graphical complexity of cartographic elements (roads, buildings, thematic data, etc.), thereby making the map readable. A zoomable map therefore allows the scale to be changed if the data representation is too complex. On the other hand, the use of generalization techniques still faces technical limitations. First, certain cartographic generalization techniques can be resource-intensive. For example, a complex displacement algorithm that manages multiple cartographic elements such as rivers, roads, and buildings to avoid collisions between them has a processing time that can be considerable, especially for datasets covering a vast area. Secondly, most current GIS and mapping software offer only a limited number of cartographic generalization algorithms, often restricted to the simplification of lines. Some projects were launched to address this issue, such as the Java CartAGen platform (designed as a plug-in for a GIS software developed and used by the French National Cartographic Agency), but these are no longer maintained and are not user-friendly. For these two reasons, cartographic generalization has been primarily limited to basic selection and simplification operators. Taking the example of road network representation, most of the base maps mentioned above select only major roads based on their attributes at small scales (level in the road network hierarchy), but without modifying the complex elements of their shape (winding roads in mountainous areas, complex structures at major road intersections, etc.).\n\nRegarding the first reason cited to explain the low use of map generalization, the literature shows that generalizing data seems to remain useful in multi-scale interactive maps [1]. As for the second reason, the availability of the CartAGen tool could encourage the adoption of advanced cartographic generalization techniques by base map providers relying on OSM data. CartAGen is an open-source Python library containing over 80 functions or methods for using generalization algorithms [2]. These algorithms are drawn from the scientific literature in the field of cartographic generalization and sometimes involve porting algorithms from other languages to Python. The library was created and is currently maintained by a research team at the IGN (the french national mapping agency), with the aim of supporting research on interactive maps and building an archive of the literature on cartographic generalization algorithms. Beyond its research applications, CartAGen aims to be used by a wider range of cartographers. CartAGen\u2019s innovative features lie in its interoperability and ease of use. The choice of Python, a language widely adopted by the GIS community, allows CartAGen to be used in various environments, such as Python scripts, Jupyter Notebook, and a QGIS plugin currently available. Integrations with other software could also be considered (ArcGIS plugin, R package, etc.). More specifically, CartAGen relies on several Python libraries already commonly used for managing geometry and spatial data formats (Geopandas and Shapely), which enhances its interoperability.\n\nThis talk at SOTM 2026 will explore several examples of generalizing OSM data using CartAGen, with the goal of designing a high quality pan-scalar map. We will focus particularly on cases where cartographic generalization reduces conflicts between map features, improves their visual hierarchy, limits clutter effect, and allows for the display of more information on the map. Indeed, these principles are likely to reduce the disorientation one may feel when navigating a pan-scalar map, thereby improving user satisfaction.\n\nBy enabling cartographers to produce better (base) maps, CartAGen could have a direct impact on how OSM data is utilized in mapping applications. Improvements in the quality of OSM data have paved the way for the emergence of mainstream mapping applications (COmaps, Organic Maps, Cartes, etc.). These open-source projects provide not only base maps but also services such as advanced route-planning features. Despite the widespread use of closed-source maps (notably the dominant position of Google Maps), CartAGen could contribute to this improvement in OSM data quality, thereby promoting the diversification of mapping projects.", "recording_license": "", "do_not_record": false, "persons": [{"code": "DYKUBN", "name": "Paul Bourcier", "avatar": null, "biography": "I am a research engineer on the [LostInZoom](https://lostinzoom.github.io/home/) project since 2025, and my current work focuses on the role of multi-scale thematic maps in crisis management.\nI also contribute regularly to the [CartAGen](https://cartagen.readthedocs.io/en/latest/) tool.", "public_name": "Paul Bourcier", "guid": "3dfafe6e-6551-5ec7-a580-cbcbf74491ef", "url": "https://pretalx.com/sotm2026-osm-science/speaker/DYKUBN/"}, {"code": "7XQJEA", "name": "Guillaume Touya", "avatar": null, "biography": "Guillaume Touya is a senior researcher, at G\u00e9odata Paris, IGN France (the French mapping agency) and Univ Gustave Eiffel. He holds a PhD and habilitation in GI science from Paris-Est University. His research interests focus on automated cartography, map generalization and volunteered geographic information. He is particularly interested in research approaches to multi-scale cartography that mix automated cartography, spatial cognition and human-computer interaction issues. He is the principal investigator of the recent LostInZoom project, funded by the Europe Research Council (ERC) (2021-2026). He is the chair of the ICA (International Cartographic Association) commission on multi-scale cartography.", "public_name": "Guillaume Touya", "guid": "2a5c8bcf-08ab-5bfd-a936-62766c710ef5", "url": "https://pretalx.com/sotm2026-osm-science/speaker/7XQJEA/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/V9ASNX/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/V9ASNX/", "attachments": []}, {"guid": "14ef08ee-83fb-5d3c-8d07-f5e50a12abb3", "code": "KUEYVB", "id": 96888, "logo": null, "date": "2026-08-29T11:50:00+02:00", "start": "11:50", "end": "2026-08-29T12:25:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-96888-reconstructing-a-high-detailed-lane-level-road-network-model-from-openstreetmap-a-connectivity-driven-approach", "url": "https://pretalx.com/sotm2026-osm-science/talk/KUEYVB/", "title": "Reconstructing A High-detailed Lane-Level Road Network Model from OpenStreetMap: A Connectivity-Driven Approach", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "OpenStreetMap centerlines carry enough topological, geometric and semantic information to reconstruct lane-level road networks \u2014 if interpreted correctly. This study proposes treating OSM centerlines as structural representations of dominant traffic flow rather than geometric midlines, and reconstructs detailed lane networks and 2D areal road geometries through a hierarchical, connectivity-driven process guided by topology, geometry, and OSM tag semantics. Applied to Delft, the Netherlands, the framework produces a complete, functionally typed lane-level network and seamless areal road representations covering diverse urban road configurations, validated against satellite imagery.", "description": "OpenStreetMap (OSM) is widely used as a foundational data source for road network analysis and transportation applications [1,2]. Its road model, however, is built around centerline geometries \u2014 efficient for routing and navigation, but not designed for lane-level detail. Existing reconstruction approaches [3,4] typically treat these centerlines as geometric midlines of road surfaces, which produces inconsistent lane connectivity and unrealistic road boundaries when pushed to higher resolution. At the same time, many transportation and simulation applications demand lane-level or trajectory-like representations [5,6]. This study investigates how far high-resolution road representations can be systematically reconstructed from existing OSM data, and under what conditions such reconstruction remains valid.\n\nThe paper's central contribution is a conceptual reinterpretation: rather than treating OSM centerlines as geometric midlines, we treat them as structural representations of dominant traffic flow. This interpretation builds on the concept of _**road strokes**_ [7,8], where adjacent segments are grouped into continuous paths based on topological connectivity [9]. Even where lane counts vary between segments, the connected sequence of centerlines preserves movement continuity by linking successive segment endpoints. The original road network \u2014 a _**directed graph**_ [10] of single centerlines \u2014 is then expanded into a _**directed multigraph**_ of lane centerlines, capturing detailed traffic flow relationships at the lane level [11]. Lane placement is derived from the underlying movement logic of the network, not assigned arbitrarily from geometry (Figure 1a). This reframing makes lane connectivity \u2014 rather than geometric position \u2014 the organising principle of reconstruction, ensuring that topology, geometry, and OSM tag semantics are addressed together [12].\n\nBefore lane reconstruction begins, the raw OSM data is pre-processed into a complete directed graph. Two-way streets encoded as single undirected edges are split into forward and backward directed edges using the `oneway=*` tag. Cycle lanes embedded within roadways \u2014 tagged as `cycleway=lane`, `cycleway:left=lane`, or `cycleway:right=lane` \u2014 are extracted as separate directed edges, since they would otherwise be invisible to the reconstruction. Lane counts and per-lane turning functions are then completed for each edge using `lanes=*` and `turn:lanes=*`, with `turn:lanes` also serving as a fallback for deriving lane count where `lanes=*` is absent. Road segments are grouped into strokes \u2014 continuous directional paths representing coherent movement routes [13] \u2014 using `name`, `highway=*_link`. Ramps as the fork from main stroke are identified and labelled by the bearing angle between the stroke flow the ramp road. The result is a directed graph of topologically clean movement paths, each carrying the lane-level attributes needed for hierarchical reconstruction.\n\nWithin each stroke, lane placement is anchored to the _**basic number of lanes (Smin)**_: the minimum number of through lanes running continuously across every segment of the stroke [14]. This basic number of lanes defines the reference from which all other lanes are positioned outward. Single-direction roads are reconstructed in three hierarchical levels (Figure 1b). First-order segments \u2014 where all lanes are through lanes and the count equals the stroke minimum lanes (_Smin_) \u2014 are placed directly from this reference with no matching required. Second-order segments share the same through-lane count but carry affiliated cycle lanes or auxiliary lanes \u2014 turn lanes, slip lanes, or merge lanes \u2014 alongside through lanes. Thus, second-order lanes inherit through-lane offsets from first-order segments, with cycle lane positions extended using `cycleway=*` to preserve cycling continuity, and auxiliary lanes offset to the corresponding turning sides. Third-order segments carry different through-lane count and auxiliary lanes, so their configurations cannot be inherited directly. A lane-matching algorithm traverses third-order segments sequentially through the stroke, finding the best-matching slice between the `turn:lanes` sequences of each segment and its predecessor. For example, where two through lanes (`turn:lanes=through|through` tag) connect to a segment with turning lanes (`turn:lanes=left|through|right` tag), the algorithm identifies which slice of the wider sequence aligns with the narrower one, determining the correct lane offset inheritance and alignment across the boundary. Bidirectional roads are processed independently: their OSM centerline is reinterpreted as the dividing line between forward and backward flows, with lanes offset to the right and left respectively. \n\nLane polygons generated from lane centerlines cannot simply be merged \u2014 adjacent polygons overlap at segment boundaries, and converging polygons at intersection nodes produce geometrically incorrect surfaces. A dedicated post-processing pipeline resolves this. A node-degree-based method identifies and trims overlapping polygons at segment boundaries. Intersection polygons are then generated at multi-segment nodes using a buffer-and-retract approach to produce smooth turning corners [15]. The post-processing ensures that the final road polygons have smooth, continuous boundaries free of spikes or abrupt geometry changes, reflecting the physical requirement of real road design. Because semantic information is preserved throughout the pipeline, each polygon type retains its traffic mode identity and can be queried or visualised independently.\n\nThe framework is tested on OSM data for Delft, the Netherlands, retrieved from public OSM extracts via the Overpass API. Pre-processing expands 393 raw centerlines into 554 directed edges, adding 41 extracted cycle lanes and 120 backward carriageways. A complete directed representation of all traffic flows is built before lane reconstruction begins, the grouped strokes are identified subsequently. Further, hierarchical reconstruction expands 393 raw centerlines into 657 typed lane trajectories, including through lanes, turning lanes, cycle lanes, and bidirectional carriageways. The positional and functional attributes of lanes are derived from OSM tags, demonstrating that a single centerline encodes enough semantic information to support detailed lane-level modelling. The results show that the framework handles a broad range of real-world urban configurations, including asymmetric turn lane layouts, embedded cycle lanes, dual carriageways with slip roads, and mixed motorized-cycleway intersections (Figure 2). Visual comparison with satellite imagery (Figure 3) confirms that reconstructed road boundaries, lane widths, multiple lane types and intersection shapes correspond to observable road structures across the study area. \n\nThis study reframes how centerline data relates to higher-resolution road models: accurate road geometry emerges from the interaction of topology, geometry, and semantics, not from geometric data alone. The results reveal a dual role for OSM tagging \u2014 it is both the primary input that enables lane reconstruction and the factor that defines its limits. Where tagging is complete, the framework produces detailed and spatially coherent lane networks. Where it is incomplete, reconstruction uncertainty cannot be resolved by topology or geometry alone, pointing to concrete directions for improving OSM tagging practices. The implementation relies entirely on open-source libraries for working with spatial networks such as shapely, OSMnx, and NetworkX. By shifting reconstruction from geometric interpretation to connectivity-driven logic, this framework expands OSM's functional capacity for traffic simulation, multimodal infrastructure analysis, and urban digital modeling, etc.", "recording_license": "", "do_not_record": false, "persons": [{"code": "T8ZFR3", "name": "Chengzhi Rao", "avatar": null, "biography": "- Current role:\nGeospatial Data Analyst at the \"Global Dynamic Exposure Model\" Group, Section 2.6 Seismic Hazard and Risk Dynamics, GFZ Helmholtz Centre for Geosciences, Germany\n\n- Research Interests:\n1. Geospatial data processing (Global building data, 3D model, 3D point clouds, Digital terrain model, Remote sensing, Transportation, Geospatial open data, etc)\n2. 3D Modelling for the built environment by using geospatial data: 3D building modelling, 2D and 3D road network modelling, etc\n3. Urban analytic\n\n- Education:\n1. Master of Geomatics, Delft University of Technology, the Netherlands (2022-2024)\n2. Master of Landscape Architecture and Urban Planning, Beijing Forestry University, China (2015-2018)\n3. Bachelor of Landscape Architecture, Beijing Forestry University, China (2011-2015)", "public_name": "Chengzhi Rao", "guid": "275ebd3e-2550-51e1-a318-5955baedcfc6", "url": "https://pretalx.com/sotm2026-osm-science/speaker/T8ZFR3/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/KUEYVB/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/KUEYVB/", "attachments": []}, {"guid": "66708105-fa60-5247-9e33-43e5b3256592", "code": "HYKXYK", "id": 97954, "logo": null, "date": "2026-08-29T12:25:00+02:00", "start": "12:25", "end": "2026-08-29T13:00:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-97954-towards-a-real-time-monitoring-system-for-the-road-network-based-on-openstreetmap-and-remote-sensing-data-osm-road-monitor", "url": "https://pretalx.com/sotm2026-osm-science/talk/HYKXYK/", "title": "Towards a Real-Time Monitoring System for the Road Network Based on OpenStreetMap and Remote Sensing Data: OSM Road Monitor", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "We present OSM Road Monitor, an approach towards a near-real-time system that cross-validates OpenStreetMap road updates against remote sensing imagery to detect and assess changes in the road network. We discuss three categories of computer vision approaches for road change detection and demonstrate a proof of concept on a selected set of orthophotos, where we showcase that a SpaceNet5-winning segmentation ensemble can successfully identify newly constructed and demolished road segments.", "description": "Up-to-date road data is essential for everyday applications such as routing and navigation. The timeliness of road data is particularly crucial in disaster scenarios when the road network is simultaneously subject to numerous changes due to destruction, flooding, or blockages [1]. However, existing routing applications can only take these changed conditions into account after a significant delay. Official data is provided with long update cycles and is not consistent across national borders. Even commercial providers need time to process their data and offer updates. This is where OpenStreetMap (OSM) can play to its strengths [2]. Thanks to the involvement of the large OSM community, the OSM database can be updated quickly. Furthermore, due to its free availability (Open Data License), OSM data is already the first choice for many humanitarian organizations and companies for map products and traditional routing applications.    \n\nNevertheless, existing applications cannot fully exploit the potential of OSM data in terms of its timeliness since detecting road changes solely based on OSM updates is sometimes inherently unreliable. This is because OSM is geographical data gathered primarily by volunteers, in which road completeness and geographic accuracy vary substantially across regions and mapping communities [3]. Also, not all changes in OSM automatically lead to an improved data foundation. While deliberate mis-mapping and vandalism are relatively rare phenomena in OSM, they have potentially serious consequences. In recent years, there has been a rise in OpenStreetMap vandalism often linked to geopolitical conflicts. Such inconsistencies motivate the exploration of cross-validation approaches, particularly leveraging computer vision (CV) models to extract road changes from satellite images or orthophotos, thereby enabling the cross-validation of results derived from OSM to enhance reliability. \n\nWhile most academic research on OSM relies on snapshots that examine specific time points, studying it in real-time induces new research questions. In our case, we want to examine to what extent the stream of OSM changes can be matched temporally and spatially with changes observed in remote sensing imagery. \n\nIt is increasingly possible to rely on high-resolution satellite data to verify changes in OSM road data, particularly in emergencies [4,5]. However, processing multi-temporal satellite data can be a time-consuming task, and there is no easy way to determine, in near real time, which data in OSM has been modified and how the relevance of these changes to routing applications can be assessed. Consequently, we are analyzing how road changes can be immediately detected and assessed using OSM and RS data together. \n\nWe propose a monitoring system for the road network based on OSM and remote sensing (RS) data: OSM Road Monitor. The system processes all changes to the OSM road network and evaluates them for errors and their impact on routing results. To achieve this, OSM Road Monitor uses road change detection in zero-shot settings using satellite imagery and orthophotos. Thereby, the area of change is identified in RS data, and this area is contrasted with the changes made in OSM. In this paper, we present the concept towards a near-real-time road monitoring system and the first results of change-event detection for identifying changes between two aerial or satellite images taken at different times. By combining change events with OSM updates, this allows us to identify and verify changes in the road network.   \n\nTherefore, we conducted a qualitative evaluation of several pretrained CV models to investigate their zero-shot generalization capabilities. The first category (C1) refers to road changes by comparing road segments of each timestamp estimated by models pretrained for road segmentation [6]. C1 methods are attractive due to the high availability of pretrained models for road segmentation. However, C1 is intrinsically sensitive to compounded error. Any false positive or false negative at either timestamp can be misread as a change. The second category (C2) uses the same strategy as C1 but employs foundation models such as the Segment Anything Model (SAM) to leverage their zero-shot generalization capabilities [7]. Despite SAM\u2019s superior performance across various datasets, its open-ended segmentation paradigm necessitates appropriate prompting techniques. Experimental evidence indicates that SAM variants with visual prompts are more suitable for road segmentation than those with pure text prompts [8]. Nevertheless, as C2 retains a similar inference pipeline to C1, it continues to suffer from compounded errors. Furthermore, the segmentation results are unreliable when changes in the OSM database are missing or incorrect. The third category (C3) directly adopts change detection by estimating change masks from two temporally different images, thereby effectively avoiding the compounded errors observed in C1 and C2. However, available pretrained models are typically optimized for building change detection rather than road network dynamics. Admittedly, changes to buildings may occasionally require updates to the surrounding road network, but this is not always the case. Furthermore, since the number of buildings significantly exceeds that of roads, the overall efficiency of this approach is theoretically low. Regardless of the methodological category, challenges inherent to the data itself should also be considered. This includes inter-temporal heterogeneity, resolution and acquisition variation, orthophoto occlusion effects such as shadow, canopy, and sheltering, and the scarcity of labeled road-change samples.   \n\nIn this paper, we present the first results that are aimed at building a demonstrator of OSM Road Monitor to form a near-real-time monitoring system for the road network based on OSM and RS data. As proof of concept, we provide exemplary results with orthophotos from the Swiss Federal Office of Topography swisstopo [9]. A pretrained model that was placed first in the SpaceNet5 challenge is used for inference [6, 10]. It comprises an ensemble of eight models, each incorporating a U-Net-style decoder with either a ResNet50 or SE-ResNeXt50-32x4d backbone, containing 73.3M and 75.3M parameters per model, respectively. Figure 1 shows road masks and segmented orthophotos for 2022 and 2025, respectively, around 47.5530\u00b0N, 8.5350\u00b0E (Tile 2682-1267) in Switzerland. A newly constructed road (a) and demolished roads around the roundabout (b) are successfully detected. Hence, this paper argues that a multimodal, data-centric strategy is essential for effective road change detection. The demonstrator is publicly available on GitHub at https://github.com/tum-bgd/2026-SOTM-osmroadmonitor.  \n\nOSM Road Monitor is envisioned as a notification system. Users of the application are notified at defined intervals or as soon as relevant changes occur in the OSM road network. The changes about which users wish to be notified depend heavily on the specific use case, including activity, vandalism, road topology, and road condition. Accordingly, the demonstrator will be extended to a dashboard that allows a user to specify a region of interest and generate the required reports on demand. Thereby, the area of change is identified in the RS imagery and contrasted with the changes made in OSM. If the number of changes in both does not align, a flagging function raises a message indicating the discrepancy. Accordingly, we aim to develop a real-time monitoring system for the road network based on OSM and RS data.\n\nThis project is funded by Zentrales Innovationsprogramm Mittelstand (ZIM) of the Federal Ministry for Economic Affairs and Climate Action under grant number 16KN113530.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BPB8LJ", "name": "Carla", "avatar": null, "biography": "I am a researcher at the Technical University of Munich at the Professorship for Big Geospatial Data Management.", "public_name": "Carla", "guid": "346da5cc-f489-53ad-88a7-48620e11d7fb", "url": "https://pretalx.com/sotm2026-osm-science/speaker/BPB8LJ/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/HYKXYK/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/HYKXYK/", "attachments": []}, {"guid": "3d0935c0-1822-5d0d-9213-70ba1d57545b", "code": "VQFXXL", "id": 96717, "logo": null, "date": "2026-08-29T14:30:00+02:00", "start": "14:30", "end": "2026-08-29T15:05:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-96717-mapping-and-using-openstreetmap-to-improve-geographic-access-to-healthcare-in-rural-madagascar", "url": "https://pretalx.com/sotm2026-osm-science/talk/VQFXXL/", "title": "Mapping and using OpenStreetMap to Improve Geographic Access to Healthcare in Rural Madagascar", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This talk presents how large-scale mapping in _OpenStreetMap_ enabled the creation of high-resolution tools to measure and reduce geographic barriers to healthcare in rural Madagascar, from optimizing community health worker deployment to planning door-to-door interventions. Building on a district-level pilot, we scaled this approach across multiple regions and estimated the resources required for nationwide mapping, highlighting how OSM can serve as a foundation for data-driven health system planning and improved access to care.", "description": "Comprehensive geographic data are essential to accurately model geographic accessibility to healthcare and to guide equitable health system planning and implementation. In low-income countries such as Madagascar, however, incomplete road and building data in global databases such as OpenStreetMap (OSM) limit the precision and operational applications of geographic accessibility models. In Ifanadiana District, we piloted a large-scale OSM mapping effort to address this gap and inform the activities of local health actors, mapping over 100,000 buildings and more than 20,000 km of footpaths via the HOT Tasking Manager.\n\nThese data enabled the development of a suite of novel, high-resolution, decision-support tools for health program managers and community health workers. First, we estimated shortest-path itineraries and travel times to primary healthcare facilities for every household in the district using routing algorithms combined with locally calibrated travel speed models derived from field GPS data and remote sensing inputs. This approach allowed us to move beyond traditional accessibility models based on Euclidean distance or friction surfaces, generating more precise, context-specific estimates of travel times that reflect the actual routes used by populations (Figure 1) [1,2]. These analyses revealed major geographic inequalities in access, with a large proportion of the population living more than one hour from primary healthcare facilities, and some areas exceeding 4-5 hours of travel time. \n\nBuilding on these results, we showed that geographic barriers persist even at the level of community health programs. Using high-resolution spatial data linked with healthcare utilization records, we quantified a strong distance-decay effect in the use of community health services, with consultation rates decreasing by approximately 28% per additional kilometer between households and community health worker (CHW) sites [3]. We then developed methods to optimize the geographic configuration of community health systems (Figure 2). While relocating existing CHW sites yielded limited improvements, adding additional sites in geographically dispersed areas was predicted to substantially increase consultation rates, highlighting the importance of adapting community health strategies to local geographic contexts.\n\nIn addition, we designed routing tools to support proactive, door-to-door care delivery by community health workers. Using OSM data and route optimization algorithms based on the Vehicle Routing Problem with Time Windows (VRPTW), we developed models to generate optimal itineraries for CHWs visiting all households in their catchment areas (Figure 3) [4]. These tools enabled detailed estimation of workload and personnel requirements for different types of interventions, such as mass distribution campaigns or proactive community case management programs. Results showed large variability in resource needs depending on geographic dispersion, with required personnel-days ranging from fewer than 15 to more than 60 per CHW catchment for mass campaigns. These analyses were integrated into an interactive e-health platform allowing program managers to visualize optimal routes, schedules, and resource needs under different implementation scenarios, thereby directly supporting operational decision-making at the local level.\n\nBuilding on this pilot, we have scaled the approach to seven additional districts in southeastern Madagascar, mapping a total area of approximately 30,000 km\u00b2\u2014comparable to the size of Belgium\u2014and adding nearly one and a half million buildings and approximately 200,000 km of transportation networks, the vast majority consisting of footpaths [5]. This large-scale effort was conducted through a combination of dedicated mapping teams and participatory approaches, using standardized workflows and tools derived from the initial pilot. Using these expanded data, we generated household-level estimates of geographic accessibility to both primary healthcare facilities and community health sites across a population of more than 2.5 million people. These analyses revealed substantial heterogeneity across districts, with between 24% and 65% of the population living within one hour of a primary healthcare facility, compared to much higher coverage for community health sites. These findings reinforce the critical role of community health systems in mitigating geographic barriers, while also highlighting their limitations in highly dispersed settings.\n\nBeyond accessibility modeling, this scale-up provided key insights into the completeness of OSM data and the feasibility of nationwide mapping. By comparing mapped data with AI-generated building and road datasets, we found that most regions of Madagascar remain poorly mapped, particularly in rural areas where accurate data are most needed. We estimated that mapping the entire country at a level sufficient to support precise, household-level accessibility modeling would require between 220 and 350 person-years of effort, corresponding to an estimated cost of approximately one million US dollars depending on assumptions and mapping strategies. These results demonstrate that exhaustive national mapping is technically feasible but requires substantial coordination, investment, and the integration of multiple approaches.\n\nThis work highlights how OSM can serve not only as a mapping platform, but as a foundational geospatial infrastructure for public health. We emphasize the importance of integrating mapping, spatial modeling, and health system data to move from descriptive analyses to actionable decision-support systems. By enabling precise measurement of geographic inequalities and supporting the design of targeted interventions, OSM-based approaches can directly contribute to reducing disparities in healthcare access and advancing progress toward universal health coverage.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BFV9CS", "name": "Andres Garchitorena", "avatar": "https://pretalx.com/media/avatars/FCAMNB_FosORMs.webp", "biography": "Andres Garchitorena is a researcher in global health and spatial epidemiology at the French National Research Institute for Sustainable Development (IRD). His work focuses on understanding and reducing inequalities in access to healthcare in low-resource settings, with a particular emphasis on Madagascar, where he has worked for nearly a decade in collaboration with the Ministry of Health and local partners.\n\nHe combines OpenStreetMap, geospatial analysis, and health system data to develop decision-support tools that inform community health programs and national health policies. His recent work explores how large-scale, high-resolution mapping can be used to quantify geographic barriers to care and optimize last-mile service delivery, bridging the gap between open mapping communities and operational public health systems.", "public_name": "Andres Garchitorena", "guid": "93f37d93-512d-567e-aeca-4dbd57e5a2cd", "url": "https://pretalx.com/sotm2026-osm-science/speaker/BFV9CS/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/VQFXXL/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/VQFXXL/", "attachments": []}, {"guid": "d1be6694-27b9-5951-83f7-dba9d7a6a41a", "code": "XHEEYA", "id": 97019, "logo": null, "date": "2026-08-29T15:05:00+02:00", "start": "15:05", "end": "2026-08-29T15:40:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-97019-inter-faceing-the-critique-a-socio-technical-perspective-on-humanitarian-mapping-with-the-hot-tm", "url": "https://pretalx.com/sotm2026-osm-science/talk/XHEEYA/", "title": "Inter-Faceing the Critique \u2013 A Socio-technical Perspective on Humanitarian Mapping with the HOT TM", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This paper revisits humanitarian mapping in OpenStreetMap from a critical socio-technical perspective. Since the 2010s, initiatives like the Humanitarian OpenStreetMap Team (HOT) have mobilized global volunteers for crisis mapping, while raising concerns about epistemic inequalities and the marginalization of local knowledge. Focusing on the HOT Tasking Manager, the study examines how participation is structured through platform design, enabling collaboration but also embedding standardization and governance logics. Drawing on data ethnography and infrastructure studies, it reassesses earlier critiques and their metabolization. The paper argues that humanitarian mapping today oscillates between empowerment and technocratic rationality amid ongoing data inequalities.", "description": "This contribution revisits humanitarian mapping in OpenStreetMap (OSM) from a critical socio-technical perspective. Since the early 2010s, initiatives such as the Humanitarian OpenStreetMap Team (HOT) have played a central role in crisis response by globally mobilizing volunteers to generate geospatial data. Focusing on the Tasking Manager (TM) as both a digital object and infrastructural interface, this study analyzes its technical, social, and semiotic dimensions within their historical and cultural contexts. Particular attention is given to how tensions in humanitarian mapping are metabolized through the interface. The Tasking Manager is approached both as a product of the complex configurations of open and collaborative humanitarian mapping and as an active agent shaping epistemic and organizational processes, following the idea that technologies stabilize social relations rather than merely reflecting them. From an infrastructural perspective, the interface is not a neutral access point but a site where norms, classifications, and workflows are negotiated and enforced (Star, 1999).\n\nThe development of the HOT TM can be situated within the broader platformization of digital humanitarianism and crisis mapping. Its emergence is closely tied to the rise of Volunteered Geographic Information (VGI), the spread of mobile telephony in the Global South (Duffield, 2016), and early open-source crisis platforms such as Ushahidi. These precursors share a reliance on digital technologies in volatile and precarious contexts, leveraging platforms to coordinate distributed participation. Platformization operates through interfaces that evolve within complex environments of crisis response, humanitarian governance, and community engagement. In this context, the HOT TM emerged from the OSM community as a \u201cboundary infrastructure\u201d that connects local and global practices, while mediating tensions around epistemic authority, participation, and political asymmetries in VGI. (Herfort et al. 2021)\n\nData ethnography, leaning on the method of ethnographic content analysis (Altheide, 1987), offers a methodological lens for reconstructing the history of the HOT TM by treating digital traces as ethnographic artifacts that reveal socio-technical figuration of phenomena. The analysis draws on internet documents such as project announcements, blog posts, and forum discussions from HOT\u2019s official channels; OSM wiki entries documenting version updates, feature rollouts, and governance debates; and OSM mapping data, including changeset histories, task logs, and project metadata dating back to 2010. This material allows for tracing the platform\u2019s origins in the 2010 Haiti earthquake response, its iterative development through volunteer feedback loops, and later expansions such as validation hierarchies and scaling for non-crisis applications like capacity building. By following these distributed inscriptions, the analysis highlights not only technical developments but also the stabilization of norms around efficiency, standardization, and remote participation within digital humanitarianism. \n\nDrawing on this material, the paper examines how critical perspectives on digital humanitarianism that emerged in the mid-2010s have been taken up, transformed, or absorbed within the mapping ecosystem. Scholars and practitioners alike have argued that such initiatives, while framed as democratizing, can reproduce inequalities, marginalize local knowledge, and align with technocratic or neoliberal approaches to humanitarian governance. (Glasze und Perkins, 2015; Schr\u00f6der-Bergen u. a., 2022) From the mid-2010s onward, skepticism toward narratives such as \u201csaving the world one map at a time\u201d (Meier, 2012) increased (Brandeusescu & Sieber, 2015). Critics pointed to reductive forms of remote mapping, the neglect of situated knowledge, inflated claims of impact, and blurred distinctions between response and prevention (Glasze und Perkins, 2015; Duffield, 2016; Turk, 2020; Madiano\u00fa, 2024). \n\nThe paper argues that the platformization, defined as digital infrastructuring that enables two or more actors to interact with one another (Srnicek, 2019), reconfigures power relations in humanitarian mapping in subtle but consequential ways. Participation appears open and democratic, anyone with internet access can contribute, but is heavily pre-configured. Project managers, often affiliated with NGOs, define mapping priorities and tasks. Satellite imagery, accessed through partnerships with commercial providers, varies in quality and availability across regions, shaping what can be mapped. Embedded biases privilege certain features, such as infrastructure, over more complex social or cultural dimensions. Hierarchies are institutionalized: novice mappers perform tracing tasks, while experienced validators audit and approve contributions.\n\nEpistemically, the Tasking Manager produces \u201cmoments of closure\u201d in which complex, lived spaces are translated into standardized, actionable data. As Burns (2014) notes, interfaces can foreclose alternative ways of knowing by enforcing schemas that sideline tacit or non-codifiable knowledge, such as oral histories or informal spatial practices. The tension between messiness and control, which is central to OSM\u2019s communing (Michel, 2024), becomes reconfigured within the platformized environment of humanitarian mapping. The TM channels this tension into structured workflows that prioritize efficiency and scalability, often at the expense of plurality and ambiguity.\nIn this sense, the Tasking Manager can be understood as an ambivalent socio-technical interface and a historically evolved core component of humanitarian mapping. It makes visible and operational the tensions inherent in digital humanitarianism, functioning as a carrier of sedimented power relations and a mechanism for their ongoing mediation. This perspective highlights the importance of treating interfaces as political objects rather than neutral tools, since technologies are shaped by the conditions and rationalities under which they emerge, yet they also actively produce and stabilize particular forms of practice, knowledge, and participation. Recognizing this dual role enables a more nuanced and historically grounded critique of digital humanitarianism. It also opens up space for imagining more equitable mapping practices that better account for local knowledge, redistribute authority, and remain attentive to the broader global ordering frameworks within which humanitarian technologies operate (Johns, 2023).", "recording_license": "", "do_not_record": false, "persons": [{"code": "USQKUZ", "name": "Charlotte Liebel", "avatar": null, "biography": "I am a cultural geographer with an enthusiasm for OpenStreetMap and the broader possibilities of open mapping. My work is driven by a deep curiosity about how technology and society co-evolve and shape one another over time. As a researcher, I focus particularly on the convergence of geospatial data, digital technologies, and social processes. I am especially interested in how these developments influence and transform societal structures, practices, and the ways in which we know the world.", "public_name": "Charlotte Liebel", "guid": "22e35af9-cf68-5b1f-a63c-086b0c60bc2f", "url": "https://pretalx.com/sotm2026-osm-science/speaker/USQKUZ/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/XHEEYA/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/XHEEYA/", "attachments": []}, {"guid": "ec7588a7-3cb3-5f0f-a788-a631b4cf9079", "code": "AQVAYA", "id": 96905, "logo": null, "date": "2026-08-29T15:40:00+02:00", "start": "15:40", "end": "2026-08-29T15:45:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96905-openstreetmap-for-intelligent-speed-limit-assistance-isa-the-youspeed-de-study", "url": "https://pretalx.com/sotm2026-osm-science/talk/AQVAYA/", "title": "OpenStreetMap for Intelligent Speed-Limit Assistance (ISA): The YouSpeed.de Study", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "YouSpeed.de investigates how OpenStreetMap speed-limit tags, area context, and daily diffs can be transformed into an offline smartphone runtime for intelligent speed assistance (ISA). The contribution is a reproducible deployment study showing that a single-file spatial SQLite bundle gives the best measured latency/update trade-off, while route-level replay shows that richer topology is not automatically better under mobile constraints.", "description": "Speed-limit information is one of the most policy-relevant yet operationally difficult elements of the OpenStreetMap road network. It is essential for intelligent speed assistance, driver warning systems, speed-aware navigation, and public-sector road-safety analysis, but it is not represented by a single complete attribute. In OSM, speed-limit evidence may appear as explicit maxspeed=* tags, inherited rule tokens such as source:maxspeed or maxspeed:type, contextual road classes, traffic-sign tags, or area-dependent legal defaults. It can also be absent, stale, or split across many short ways. For mobile driver assistance, this creates a scientific and engineering problem: an application must not only query OSM quickly, but also preserve provenance, infer missing context, update from daily map changes, and remain reproducible enough that claims about performance and completeness can be independently checked.\n\nThis study presents YouSpeed.de, an offline-first smartphone speed-limit runtime built from OSM extracts, as a deployment-oriented investigation of that problem. The aim is not to demonstrate a consumer application in isolation, but to answer a narrower research question: which OSM-derived data structures and update mechanisms make country-scale, low-latency speed-limit inference feasible on commodity smartphones while preserving traceability to the original map objects? The contribution is a reproducible benchmark and artifact pipeline that treats OSM as a mutable runtime knowledge base rather than as a static background map.\n\nThe methodology combines four empirical components. First, we scan European Geofabrik country extracts and measure explicit maxspeed=* coverage on car-drivable ways, separating direct speed evidence from data that requires rule-aware fallback. Second, using a Germany OSM snapshots, we generate four runtime architectures with identical source semantics: S1, a global-index baseline; S2, a spatially tiled content-addressed pack; S3, a single-file SQLite database with RTree spatial indexing; and S4, a SQLite variant with tile-membership prefiltering. The architectures are evaluated with three maxspeed query modes plus built-up-area containment at a fixed Berlin probe point, so that storage effects can be compared under a common workload. Measurements are taken both on a cloud-like Apple M4 Max host and on a physical iPhone 14 Pro to expose mobile sandbox costs. Third, we analyze a month of consecutive Germany daily OSM diffs, quantifying changed ways, speed-tag events, partition invalidations, simulated SQL patch runtimes, and payload sizes. Fourth, we evaluate several matching approaches on recorded logs covering 42,654 GPS fixes and 29,411 hindsight labels, so that architectural choices can be compared with route-level matching cost rather than lookup latency alone.\n\nThe first finding is that explicit speed-limit completeness varies strongly by country and cannot be reduced to extract size. The Netherlands leads the current scan with 0,92 million explicit-speed ways out of 1,36 million car-drivable ways, or 68 percent. Germany ranks eighth with 2,63 million explicit-speed ways out of 7,96 million, or 33 percent. France has 1,49 million explicit-speed ways out of 7,11 million, or 21 percent. These figures show why an OSM-based speed assistant needs provenance-aware fallback logic and area-context lookup: in many jurisdictions, the majority of drivable ways do not carry direct numeric speed limits.\n\nThe second finding is that physical packaging matters as much as spatial indexing. In the Germany benchmark, we investigate four storage approaches in terms of file size and query latency and derive a concrete systems conclusion: for the current OSM speed-limit runtime, a single-file embedded spatial database is the best measured deployment default. This result is not merely an implementation preference. It shows that mobile OSM applications with frequent lookups need artifact designs that respect operating-system file handling, local database performance, and update granularity, not just spatial-index theory.\n\nThe third finding concerns OSM's update dynamics. The practical implication is that daily OSM change ingestion is not only a backend concern: update granularity and mobile artifact design determine whether open map freshness can reach offline clients without excessive bandwidth or file-management overhead. In this setting, reproducible diff analysis is part of the scientific result, because speed-limit assistance depends on both low-latency lookup and the ability to keep OSM-derived artifacts current.\n\nThe route-level replay results refine, rather than overturn, the data-layer conclusion. We evaluate several approaches for matching GPS fixes to OSM road segments. The strongest lightweight matcher profile achieves 94 percent replay accuracy with a p95 query latency of 8.5 ms. A comparison with the  state-of-the-art routing-engine Valhalla shows similar matching performance, but also indicates that the richer topology of Valhalla requires more space and is not automatically better when storage size, updateability, traceability, and mobile latency are measured together.\n\nThe study's scientific contribution is therefore threefold. It provides a quantitative view of European OSM speed-limit provenance, a reproducible architecture benchmark for transforming OSM speed and area context into mobile runtime artifacts, and a deployment-facing analysis that links lookup latency, daily diff behavior, and lightweight map matching. The practical benefit for the OSM community is a concrete feedback loop: local observations can be captured on device, reviewed after the drive, exported as editor-oriented .osc change packages, and later reconciled through normal OSM diffs, rather than uploaded automatically by an opaque app pipeline. Our reproducible conclusion is that OSM can support offline speed-limit assistance when speed provenance, jurisdictional fallback, artifact packaging, and update mechanics are evaluated as one system.", "recording_license": "", "do_not_record": false, "persons": [{"code": "RKETMZ", "name": "Raphael Volz", "avatar": null, "biography": "Raphael Volz (born 1976) is a German computer scientist, full professor of Applied Informatics at Hochschule Pforzheim (Pforzheim University of Applied Sciences) since 2012, and a notable contributor to early Semantic Web research, ontologies, and knowledge engineering. With an h-index of 40 and over 8,000 citations, his work includes highly cited publications on ontology mapping, description logics, and Semantic Web technologies. He is also an entrepreneur who founded multiple technology companies and an inventor holding patents in areas such as location-based profiles and traffic sign recognition.", "public_name": "Raphael Volz", "guid": "65ed42de-c43b-5e08-9017-ffe2a48952ba", "url": "https://pretalx.com/sotm2026-osm-science/speaker/RKETMZ/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/AQVAYA/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/AQVAYA/", "attachments": []}, {"guid": "ab54e01d-fc49-5f17-a8ea-32856cb19207", "code": "EYA7UN", "id": 96990, "logo": null, "date": "2026-08-29T15:45:00+02:00", "start": "15:45", "end": "2026-08-29T15:50:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96990-openstreetmap-for-travel-demand-modeling-an-open-approach-to-derive-attractiveness-for-destination-choice-models", "url": "https://pretalx.com/sotm2026-osm-science/talk/EYA7UN/", "title": "OpenStreetMap for Travel Demand Modeling: An Open Approach to Derive Attractiveness for Destination Choice Models", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "OpenStreetMap data are used in travel demand modelling not only to derive transport networks, but increasingly also to quantify the attractiveness of traffic analysis zones in destination choice modeling. Our paper presents this domain-specific use case and examines which processing steps are particularly suitable for deriving robust attractiveness measures from OSM data. We describe the processing workflow and discuss the usability for modelling purposes. The developed workflow has been implemented in code and is published on GitHub to support transparent and reproducible use.", "description": "**Introduction and Motivation**\nTravel demand models are an essential tool in transport planning because they are used to estimate the impacts of changes in transport supply, land-use developments, and related interventions. Most travel demand models are based on the logic of the four-step model, according to which travel demand emerges from four interrelated decisions. In the first step, individuals decide which activity (e.g. work, education, daily shopping) they wish to perform and at what time. Based on this, they choose a destination for that activity. In the third step, they select a transport mode, and finally, they determine a route along the road network or via public transport. [1]\nTravel demand models require large amounts of data. These include travel behavior and socio-demographic data, but also information on travel times between origins and destinations and, crucially, on where destinations are located. OpenStreetMap (OSM) has become a popular data source in transport modeling to retrieve street and path networks for routing and travel time calculations. In recent years its role has expanded towards a second and equally important question: where are the destinations that generate trips, and how attractive are they for different activity types? A small greengrocer, for example, should not be assumed to attract the same number of trips as a large supermarket.\nTraditionally, such data have been collected manually or purchased from commercial providers. While such data products may offer high quality, their sources and processing steps are non-transparent. Moreover, data for an entire study area can be expensive. OSM has therefore become an attractive alternative. Yet current use of OSM in travel demand modeling remains heterogeneous and is rarely documented. The aim of this study is to show how domain-specific value can be created from OSM data in transport science by presenting a transparent and reproducible data processing pipeline for deriving destination attractiveness data. The workflow has been implemented in Python and R and is available as the open-source tool _OSM2Attractiveness_[2].\n**Methodology**\nThe method consists of several consecutive steps and is illustrated in Figure 1. First, OSM elements are assigned to one or more activity types. Activity types categorize similar occasions and contexts of activities, such as work, leisure, etc. For this purpose, a tag list is created for each activity type (e.g. Figure 2), and the OSM data are filtered accordingly.\nSecond, the area and exact type of the resulting points of interest (POI) are determined which is necessary for the estimation of trip attraction [1]. If a way or relation represents a building, the area can be calculated from its polygon; if the number of floors is available, the floor area can also be derived. If an activity-related area is mapped outside buildings, such as a zoo or playground, the land area may already be a suitable measure. In other cases, especially when activities are usually conducted inside buildings, land area alone is insufficient, and the methodology therefore assigns buildings within the area and derives their usable floor space, for example for hospitals or schools. Specific cases must also be considered when many destinations are located within a larger destination complex, e.g., multiple shops within a shopping mall.\nIf a location is mapped only as a node, attractiveness may still be estimated by considering the exact type of the POI. Where a space-related measure is required, e.g., for shopping destinations, two fallback strategies can be applied: using the surrounding building area or assigning a typical average size based on the store name or brand.\nThird, attractiveness respectively trip attraction is calculated, which is defined as the expected number of trip ends attracted by a destination per day. Simply using floor areas is not sufficient because different facility types generate different amounts of travel. A car dealership generates substantially fewer trips per square meter than a supermarket. The calculation requires trip attraction rates that describe the relationship between POI counts or floor space of an exact type and trip ends. In Germany, one relevant source is _VerBau_ [3]; comparable datasets exist for the United States, Great Britain, Australia, New Zealand, and other countries [4-6]. It should be noted that the calculated attractiveness is not the actual number of daily visitors. The real number of trips to a destination additionally depends on the distance between trip origins and other destinations within the gravity-based destination choice model [7]. In the end the results are aggregated on the level of traffic analysis zones (TAZ).\n**Findings**\nThe methodology has been applied successfully in several German case studies, including Hamburg, Karlsruhe, and Stuttgart, using the open-source agent-based travel demand model mobiTopp [8]. Across these applications, the results were evaluated by visually inspecting the resulting attractiveness distributions at the level of TAZ. This iterative plausibility assessment proved to be an important part of the workflow, because it allowed systematic refinement of the process. In addition, data quality and completeness were assessed for the intended use. The results indicate that some categories can be used more robustly than others [9].\nThe resulting attractiveness data were suitable for operational model use and demonstrated that OSM can support not only network modeling but also destination modeling in a domain-specific and reproducible way. Aggregated results for the Hamburg study area are shown in Figure 3 as an example [10].\n**Discussion and Summary**\nWe developed an approach to calculate the attractiveness of TAZ for a wide range of activity types using OpenStreetMap data. The same methodology can also be applied not only to zones but to individual POIs, which opens opportunities for more disaggregated destination choice modeling.\nAt the same time, the study also highlights limitations. Reasonable attractiveness measures require a certain level of data quality. Multi-story buildings, for instance, often lack information on the number of floors and the inner distribution of types of use, which limits the quality of floor-space-based estimates. We therefore recommend complementing OSM, where necessary, with additional datasets such as building models or brand-specific reference values. Furthermore, data completeness differs strongly between activity types. Shopping-related information is often relatively stable, whereas private business or specific service functions may be more unevenly covered [10]. Nevertheless, for some activity types we found OSM to be more complete and more up to date than official data sources. We therefore recommend that trust in OSM and official sources be evaluated separately for each activity type and region.\nA major contribution is the creation of a transparent and transferable workflow that allows modelers to use OSM more systematically. Because OSM is available worldwide, the approach can be extended not only to additional activities but also to many other regions, including cross-border study areas. In the longer term, the structured nature of OSM data also creates opportunities for stronger automation, faster model updates, multi-temporal analyses, and transport-oriented quality metrics. For the OSM community, this paper demonstrates a concrete and scientifically grounded domain-specific use case.\n**Acknowledgements**\nWe gratefully acknowledge the OpenStreetMap community and all contributors whose continuous mapping efforts make this work possible.\nThe map-based figures in this paper use data from OpenStreetMap available under the Open Database Licence (ODbL).", "recording_license": "", "do_not_record": false, "persons": [{"code": "MEZ7HS", "name": "Christian Klinkhardt", "avatar": "https://pretalx.com/media/avatars/WBHZJ8_lxu70b3.webp", "biography": "Christian Klinkhardt is an academic researcher at the Institute for Transport Studies at KIT and a city councillor on the Municipal Council of the City of Karlsruhe. He studied civil engineering at KIT, completing his master\u2019s degree in 2020. His research focuses on travel demand modelling, the integration of open data into transport models, and the deployment of autonomous driving in our transport system.", "public_name": "Christian Klinkhardt", "guid": "e913a369-46ea-5085-a90a-3a796dd645b2", "url": "https://pretalx.com/sotm2026-osm-science/speaker/MEZ7HS/"}, {"code": "KTCSDV", "name": "gabrielwilkes", "avatar": null, "biography": null, "public_name": "gabrielwilkes", "guid": "7e275dac-f02a-5263-9225-9a94426b0919", "url": "https://pretalx.com/sotm2026-osm-science/speaker/KTCSDV/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/EYA7UN/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/EYA7UN/", "attachments": [{"title": "Presentation_Paris_260829", "url": "/media/sotm2026-osm-science/submissions/EYA7UN/resources/OSM_f_gQO0YGi.pdf", "type": "related"}]}, {"guid": "8effb7b3-0898-5a60-97d9-bb33b84c149b", "code": "LA3WRV", "id": 96118, "logo": null, "date": "2026-08-29T15:50:00+02:00", "start": "15:50", "end": "2026-08-29T15:55:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96118-public-transport-stop-analysis-for-osm-and-welzl-s-algorithm-on-the-sphere", "url": "https://pretalx.com/sotm2026-osm-science/talk/LA3WRV/", "title": "Public transport stop analysis for OSM and Welzl's algorithm on the sphere", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "An introduction to the public transport stop analysis (PTSA) project for visualizing public transport mapping habits around the world and a closer look at how to efficiently and accurately measure lots of distances between neighboring OSM objects.", "description": "Mapping public transport stops in OSM is a complex and hotly debated topic. Requirements from different data consumers like individuals, transport agencies, routing apps and renderers meet a range of still evolving mapping standards and conventions. In the talk, on the one hand, we give an introduction to the public transport stop analysis (PTSA) project [1] for visualizing public transport mapping habits around the world and for pointing mappers to inconsistent and possibly erronous data. The data aggregated and visualized by PTSA may inspire further evolution of OSM's public transport mapping schemes. At least it enables local mappers to adapt to their local community's preferred mapping scheme and to improve data quality. On the other hand, we take a look at some algorithmic problems behind the scenes, which motivated the development of a new algorithm for efficient distance measurements with guaranteed and adjustable minimum accuracy. Published source code and documentation may help similar projects to scale ideas and code from a local level to the whole OSM planet.\n\nMapping of public transport stops in OSM evolved from simple nodes to complex combinations of nodes, ways and areas. Conventions like the classical (aka PTv1) and the PTv2 scheme for public transport mapping do not allow for a clear and simple grouping of all relevant objects. The smallest group available is a stop area, which usually comprises all OSM objects of several nearby or otherwise related stops. Renderers and similar data consumers somehow have to figure out whether multiple objects, say a node and a nearby way (platform), represent one and the same stop or whether there are two or maybe even more stops to render. PTSA provides such stop-level grouping by analyzing geometry and tags of all OSM objects related to public transport. PTSA thus not only allows to visualize different mapping styles around the world, but also yields lots of hints on inconsistent data. Analyzing data aggregated by PTSA would be worth a study on its own from the social point of view. Where did mappers find a (local) consensus on how to map public transport and where not? Which regions tend to complex mapping schemes, which to simple ones? Where the classical PTv1 scheme is preferred? Where did mappers switch to the more recent PTv2 scheme?\n\nBut the talk's focus is on the technique behind the scenes. Analyzing geometry of public transport stops requires lots of distance measurements in the range for 1 to 500 meters. How to do this efficiently, but sufficiently accurate with guaranteed minimum accuracy? Accurate distance measurements on the WSG84 ellipsoid or even an a sphere are based on computationally expensive trigonometric functions. We present a new method to solve this problem without trigonometric functions, while maintaining a prescribed minimum accuracy: divide the world's landmass into regions of prescribed maximum diameter, then for each region find an optimal map projection and use Euclidean distances on the projected map.\n\nDividing the OSM planet into regions small enough to allow for low distortion map projections is a problem of interest on its own. But it's not as simple as one might think. We have to take care of not cutting through an area of interest (public transport stops). Cutting along admistrative boundaries solves this problem. Corresponding data sets for different maximum diameters have been published by the author along with the code to produce subdivisions of the planet for arbitrary maximum diameters, see [2] and [3].\n\nFor each region finding the optimal (that is, lowest distortion) map projection is another problem of general interest. If we restrict attention to azimuthal equidistant projections, then optimal mapping parameters are given by center and radius of the region's smallest enclosing circle. To compute the smallest enclosing circle of a region we have to answer two questions: How can we do this? And: how fast can we do this? Computations have to be done on the sphere, not in the plane, to get optimal mapping parameters. On the sphere there only exist algorithms with quadratic time complexity or other shortcommings. An administrative boundary in OSM may consist of more than 100000 nodes (e.g. France and Germany). So the algorithm's time complexity really matters. In the plane there's Welzl's linear time algorithm for smallest enclosing circles [4]. We present a new (hemi-)spherical version of Welzl's algorithm, which maintains linear time complexity and resolves some of the shortcommings of other algorithms, see [5]. A ready to use implementation is available on PyPI [6].\n\nThe combination of small regions and the optimal projection parameters obtained from our new Welzl-type algorithm allows for Euclidean distance measurements on the map, while still maintaining a prescribed accuracy. Accuracy can by expressed by the deviation between Euclidean distances on the projected map and geodetic distances on the WSG84 ellipsoid. Given a desired accuracy we show how to compute the corresponding maximum diameter for regions in the subdivision of the OSM planet.", "recording_license": "", "do_not_record": false, "persons": [{"code": "VG7DWX", "name": "Jens Flemming", "avatar": null, "biography": "Teaching and research in math, computer science, data analysis", "public_name": "Jens Flemming", "guid": "1b89bef4-b847-55f6-bfda-59d6f915461f", "url": "https://pretalx.com/sotm2026-osm-science/speaker/VG7DWX/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/LA3WRV/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/LA3WRV/", "attachments": [{"title": "Slides", "url": "/media/sotm2026-osm-science/submissions/LA3WRV/resources/talk_TLx2Clv.pdf", "type": "related"}]}, {"guid": "49242359-8965-59be-a385-04f331a1ed07", "code": "FPWSB8", "id": 98109, "logo": null, "date": "2026-08-29T15:55:00+02:00", "start": "15:55", "end": "2026-08-29T16:00:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-98109-assessing-the-intrinsic-data-quality-of-openstreetmap-for-national-scale-cycling-network-analysis", "url": "https://pretalx.com/sotm2026-osm-science/talk/FPWSB8/", "title": "Assessing the Intrinsic Data Quality of OpenStreetMap for National-Scale Cycling Network Analysis", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This paper presents a scalable, relation-aware framework for national-scale cycling network analysis by combining the ohsome platform and DuckDB to process the complete Dutch cycling network from OpenStreetMap. It enables efficient, reproducible evaluation of intrinsic data quality and infrastructure characteristics across hierarchical route relations, supporting consistent assessment of cycling network completeness, connectivity at national scale.", "description": "Cycle route networks are increasingly recognised as a critical component of national and regional mobility strategies. The 2025 UNECE Guide for Designating Cycle Route Networks [1] (UNECE, 2025) underscores the importance of coherent, hierarchically integrated cycling infrastructure (from international backbones such as EuroVelo down to local networks) capable of serving both everyday commuters, leisure and tourism users. Crucially, the [1] UNECE Guide (2025) makes explicit that realising this vision is inseparable from data: it calls for assessments to be data-driven, drawing on multiple sources to evaluate the appropriateness of existing roads and alternative corridors for cycling. Against this background, OpenStreetMap has the potential to be a significant source of cycling network data for exactly this purpose, offering global coverage, granular way-level attributes, and a relational structure capable of representing network hierarchies from international to local scale. \n\nYet OSM data quality is known to be spatially and thematically uneven. Previous literature has consistently demonstrated that OSM quality varies significantly across regions, feature classes, and mapping communities [2-5] (Hakley, 2010; Hochmair et al. 2015; Guth et al., 2021; Neis et al., 2013). In the context of cycling infrastructure, this unevenness implies that the capacity to assess network quality is itself spatially and thematically uneven, varying across regions and attribute types [6-7] (Vier\u00f8 et al., 2024; Ferster et al., 2020). Consequently, any assessment of cycling network quality is inherently constrained by where data are more complete or sparse, and by how consistently key attributes are recorded.\n\nAdopting this perspective requires that quality assessment be both spatially disaggregated, examining variation across the territory rather than relying on global aggregates, and thematically targeted, focusing on the attributes and network properties that are most relevant to the analytical task.This is particularly important in the context of cycle route planning, where requirements concerning safety, continuity, comfort, directness and infrastructure separation vary across user groups, route hierarchies and expected traffic volumes, making uniform or purely aggregate assessments insufficient for meaningful evaluation and decision-making [1] (UNECE, 2025).\n\nHowever, doing so at scale is not straightforward. Much of the information relevant to cycling network quality (infrastructure type, surface, legal access conditions) is stored at the way level but only meaningful when understood through bicycle relations, which define the network hierarchies that planning and certification frameworks operate on. Conducting a country-level, relation-aware quality assessment has historically required substantial preprocessing that constrained both the scale and depth of such work [8-9] (Deri et al., 2015; Pruvost et al., 2017). This paper demonstrates that this constraint can be overcome.\n\nThe analysis uses the ohsome planet tool developed by HeiGIT, which converts the full OpenStreetMap planet into an analysis-ready Parquet dataset while preserving its complete relational structure [10] (HeiGIT, 2025). Combined with DuckDB for fast and scalable querying, this setup enables a fully reproducible analysis pipeline for the entire Dutch cycling network. The dataset covers all levels of the network hierarchy, including 22 international route relations that form the backbone of the system, as well as 118 national, 16,964 regional and 264 local route relations that support mobility. Overall, the analysis includes 183,147 ways representing approximately 39,455 km of roads and 17,368 route relations (Figure 1).\n\nCentral to the paper is a two-phase analytical framework that makes the relationship between OSM data quality and the assessment of cycling network quality explicit. \n\nIn the first phase, the focus is entirely on what the OSM data itself can and cannot support. Tag completeness is measured across four attributes central to cycling infrastructure evaluation: surface type, lighting, width, and maximum speed (Figure 2). The internal consistency of OSM tagging conventions is tested. Topological connectivity is assessed using NetworkX across each network hierarchy, examining the degree to which relations form coherent, traversable networks rather than fragmented collections of ways (Figure 3).\n\nThe second phase proceeds only where the first establishes that the data permits it. Where tags are sufficiently present and internally consistent, infrastructure attributes are evaluated against established external standards, specifically the [1] UNECE Guide (2025) and the EuroVelo European Certification Standard [11] (ECF, 2021). These provide concrete, operationalisable criteria: minimum width thresholds differentiated by route hierarchy, surface quality tiers appropriate to infrastructure type, and the relationship between posted speed limits and the level of physical separation required to make cycling conditions acceptable. Grounding the second phase in normative standards rather than data-derived benchmarks produces assessments that are externally meaningful rather than self-referential.\n\nTogether, the two phases allow the paper to make statements not only about patterns of OSM data quality across the Netherlands, but about the quality of the bicycle network itself, where the data supports such a conclusion. In doing so, the paper foregrounds the role of bicycle relations as the structural backbone that links way-level attributes to network-level meaning, and without which neither the completeness assessment nor the evaluation against external standards would be possible at a meaningful scale.", "recording_license": "", "do_not_record": false, "persons": [{"code": "J9ACXZ", "name": "Vanessa Bolognani", "avatar": null, "biography": "MSc Student in Geographical Information Sciences", "public_name": "Vanessa Bolognani", "guid": "23828ff7-bac6-5daa-bf2f-0b309edf365e", "url": "https://pretalx.com/sotm2026-osm-science/speaker/J9ACXZ/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/FPWSB8/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/FPWSB8/", "attachments": [{"title": "Assessing OSM data quality at scale starting from bicycle route relations", "url": "/media/sotm2026-osm-science/submissions/FPWSB8/resources/Light_BvOTf3j.pdf", "type": "related"}]}, {"guid": "3664a5e4-ce1a-5ee8-a3ac-c8e77068a616", "code": "CAPXCT", "id": 98112, "logo": null, "date": "2026-08-29T16:00:00+02:00", "start": "16:00", "end": "2026-08-29T16:05:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-98112-openstreetmap-data-for-spatial-regression-modeling-of-urban-operating-speed", "url": "https://pretalx.com/sotm2026-osm-science/talk/CAPXCT/", "title": "OpenStreetMap Data for Spatial Regression Modeling of Urban Operating Speed", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This study evaluates how well OpenStreetMap (OSM) data can explain urban operating speeds using spatial regression models in Curitiba, Brazil, combining naturalistic driving data with socioeconomic and built-environment variables. The results show that OSM-derived variables, especially road network density and traffic signals, are useful for identifying spatial patterns in speed, but their limited detail reduces predictive accuracy, making OSM more suitable for exploratory large-scale safety analyses than precise modeling.", "description": "The increasing availability of Volunteered Geographic Information (VGI) has enabled new approaches for modeling urban dynamics, particularly in road safety studies on speed management[1]. Despite the growing use of OpenStreetMap (OSM), there is still limited evidence regarding its adequacy to support spatial models of operating speed in urban environments. Across multiple studies, road width, number of lanes, segment length, and distance to intersections consistently predict higher operating speeds. Longer continuous segments and multi\u2011lane roads increase the 85th-percentile speed [2]. Narrower streets and visually constrained environments reduce speeds [3]. Curves and gradients lower speeds, particularly on residential streets [4,5]. At the same time, naturalistic driving datasets, such as the Brazilian Naturalistic Driving Study (NDS-BR), provide detailed observations of driver behavior but often lack structured spatial variables describing the built environment [6]. This gap raises the following research question: to what extent can OSM-derived variables explain spatial variations in operating speed, and how suitable are they for spatial regression modeling in speed management analysis?\nThe main objective of this study is to evaluate the explanatory capacity of OSM-derived variables in modeling the 85th-percentile operating speed (V85) of private vehicles and to compare the performance of global spatial regression models. Specifically, the study assesses whether OSM data can provide statistically and spatially meaningful predictors and examines the implications of their limitations for urban road safety studies.\nThe analysis was conducted in Curitiba, Brazil. NDS-BR collected data from 52 drivers\u2019 routine activities to study road safety for approximately two weeks for each driver. The study utilized GPS devices to gather data without any interference or interaction from researchers regarding routes or destinations between the years of 2019 and 2023. A regular grid of 200 \u00d7 200 meters was used as the unit of analysis, integrating operating speed data with socioeconomic variables from the Brazilian Institute of Geography and Statistics (IBGE) and built-environment variables extracted from OSM. This is the standard grid made available from IBGE for statistical analysis and allows similar approaches to be used in other areas of the country. The dependent variable (V85) was calculated from observed vehicle speeds within each grid cell. Due to the nature of the data acquisition process, some cells within the grid were not fully populated with information. The total number of grid cells containing information on inhabitants and households within the entire boundary of Curitiba was 8,956. For analysis and interpretation, grid cells lacking recorded speed data were excluded, resulting in a total of 5,456 grid cells. Independent variables included IBGE database such as population and number of households and OSM database that includes road network length (interpreted as street density as it refers to the sum of all road segments within each grid square), bus stops, number of intersections, traffic signals and speed cameras.\nThe software utilized for this research were all free and open-source such as R (for statistical analysis and data manipulation) QGIS (for data visualization) and GeoDa (for spatial statistics). Three regression approaches were implemented: Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM). Spatial dependence was modeled using a contiguity-based spatial weights matrix of first order. The dataset was split into training (75%) and testing (25%) subsets for validation. Multicollinearity diagnostics led to the exclusion of highly correlated variables. High values were identified for households and inhabitants (0.97) and for intersections and extension (0.55). So, alternative model specifications were tested.\nThe OLS results indicated a statistically significant model (p-value < 0.001), but with low explanatory power (R\u00b2 approx. 0.10), suggesting that linear relationships alone are insufficient to explain spatial variations in operating speed. This is common in complex behavioral phenomena, or in the present case where there is a spatial component that is not adequately accounted for by the model. Among the predictors, road network density and traffic signals showed consistent negative associations with V85, indicating that denser and more regulated urban environments tend to reduce operating speeds. However, variables such as bus stops and speed cameras were not statistically significant (p-value > 0.05) and were excluded from subsequent analyses.\nSpatial models improved model fit substantially. The Spatial Lag Model presented the best performance, with lower Akaike Information Criterion (AIC) and higher log-likelihood values compared to OLS and SEM, indicating that spatial interaction effects play a significant role in explaining operating speed. Nevertheless, even with spatial dependence accounted for, the overall explanatory power remained limited, with prediction errors (RMSE approx. 14.8) indicating considerable unexplained variability.\nThe values obtained (R\u00b2, RMSE) are likely influenced by the grid configuration used, reflecting the Modifiable Areal Unit Problem. Since each grid cell may aggregate road segments with distinct geometric and functional characteristics, this can reduce the model\u2019s explanatory power by masking local variability. So far, the results highlight both the potential and the limitations of OSM data in road safety modeling. On one hand, OSM provides relevant spatial variables such as network density and traffic control elements that are statistically associated with operating speed and can support spatial regression analyses. On the other hand, the absence of critical attributes identified in the literature (e.g., lane width, road geometry, traffic volume, and detailed functional classification) restricts the explanatory capacity of the models. This suggests that OSM, in its current state, is better suited for exploratory and large-scale analyses rather than high-precision predictive modeling.\nFrom a scientific perspective, this study contributes by providing an empirical assessment of the suitability of OSM data for spatial modeling of operating speed, explicitly quantifying its limitations within a regression framework. From a practical standpoint, the findings inform researchers and practitioners about the conditions under which OSM data can be reliably used in urban safety studies and highlight the need for data enrichment strategies, whether through improved mapping practices or integration with complementary datasets.\nFuture work will focus on incorporating local spatial models, such as Geographically Weighted Regression, and machine learning approaches, as well as evaluating the impact of OSM data completeness and quality on model performance.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BXPLCG", "name": "Rafael Szeliga", "avatar": "https://pretalx.com/media/avatars/3DH83H_PPos40p.webp", "biography": "As a civil engineer with over 15 years of experience, I focus on transportation engineering and infrastructure development. I contribute to diverse projects in transport engineering, including geometry design, drainage, pavement, and earthworks. My expertise also extends to urban mobility consulting and traffic engineering, utilizing advanced traffic microsimulation tools for impact studies on traffic-generating hubs.  \n\nMy technical proficiency includes geoprocessing using QGIS, statistical analyses with R programming, and foundational knowledge in Python and machine learning. Currently pursuing a doctorate in Geodetic Sciences (cartography field) at Universidade Federal do Paran\u00e1, I am committed to exploring innovative solutions for urban planning and mobility and road safety challenges.", "public_name": "Rafael Szeliga", "guid": "fe146a9e-92a8-5792-a9e0-51458c77f073", "url": "https://pretalx.com/sotm2026-osm-science/speaker/BXPLCG/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/CAPXCT/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/CAPXCT/", "attachments": [{"title": "Presentation", "url": "/media/sotm2026-osm-science/submissions/CAPXCT/resources/OSM_S_ir2rXEM.pdf", "type": "related"}]}, {"guid": "1ab86be1-17a6-500d-9eee-869cc88ecdcc", "code": "FLYV3H", "id": 98160, "logo": null, "date": "2026-08-29T16:45:00+02:00", "start": "16:45", "end": "2026-08-29T17:20:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-98160-whose-knowledge-counts-analysing-diversity-and-exclusion-in-openstreetmap-s-tagging-proposals-process", "url": "https://pretalx.com/sotm2026-osm-science/talk/FLYV3H/", "title": "Whose Knowledge Counts? Analysing Diversity and Exclusion in OpenStreetMap's Tagging Proposals Process", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This talk analyses how gender inequality shapes OpenStreetMap's (OSM) tagging proposal process, revealing that diversity gaps intensify at higher participation levels. Maps are never neutral \u2014 their makers' demographics determine whose realities are represented or silenced. Systematically analysing every tagging proposals and votes since its creation using a purpose-built R package, we found that 59 prolific, mostly European and North American male users dominate proposal approval, while feminised proposals are underrepresented and disproportionately opposed. These findings confirm that structural inequities persist regardless of how well-designed, flexible and well-intended technologies may be.", "description": "OpenStreetMap (OSM) creation in 2004 marked a political, technical and epistemic revolution to democratise the production and visualization of geographic data, pioneering a movement that would later become to be known as Volunteered Geographic Information (VGI)[1]. Many saw in VGI in general, and OSM in particular, as an opportunity to address systemic problems in cartography derived from the historical linage mapping as a means for elites to exert power[2]. Critical and Feminist Geographers have long contended that maps are embodied subjects[3,4] and cultural expressions[5], rather than mere representations. Consequently, maps are never objective or neutral[6], and the historical lack of diversity in map-makers[7] results in biased worldviews when it comes to decide what it is included and silenced[8\u201310] in cartographic representations, thus reproducing and amplifying further inequalities.\nOSM\u2019s signature features (e.g. its global community, open data model and, particularly, its crowdsourced and non-hierarchical approach), provide an ideal sociotechnical infrastructure to overcome those issues. By incorporating a myriad of different worldviews as a result of the ability for anyone to add anything they find relevant to the map, OSM is well-placed to be a cornerstone in the production of equitable maps to allow for other forms of cultural expressions, leading to greater forms of social justice[11].\nHowever, against this techno-optimistic rhetoric (so frequent in the advent of the Web 2.0 in the early 2000s, of which OSM is a paradigmatic example), many scholars and OSM members have flagged that the lack of diversity in the community is still a concern [12\u201315], despite community efforts to improve the situation. This paper contributes to the corpus of previous literature which has surfaced how factors such as gender, ethnicity or sexual orientation translate into different patterns of contributions. However, previous research has primarily focused only on a particular type of contribution: map edits. While, arguably, this is probably the most attractive and, therefore, the most prominent way of contributing to the project, there are many other ways of doing so (e.g. documenting, participating on community forums, translations or participating in the project\u2019s governance). In this paper, we will be \u201cembracing pluralism\u201d[9(Chapter 5)] by focusing on a different and often overlooked way of contributing to OSM: the tagging proposal process [16], which is the formal process to introduce and discuss how a real-world situation (i.e., a feature) is incorporated (or not) to OSM\u2019s data model[17]. We do so by systematically analysing all the 2,115 tagging proposals that have been documented since 2006 and the 8,487 votes they have received. To allow for a systematic and reproducible analysis, as well as for scrutiny, we created an R package[18] (to be published alongside this paper) that programmatically retrieves all the tagging proposals listed in the OSM Wiki using a combination of API queries and web scrapping. For each of those pages, we retrieved the associated metadata, as well as all the votes received by each of the tagging proposals, and recorded the vote (either \u201cApprove\u201d, \u201cOppose\u201d or \u201cAbstain\u201d), the detailed rationale (if any), and the user who emitted the vote. The  two separate documented datasets[19] produced by this package constitute the primary data source for this paper. From this data, we describe and critically engage with how the tagging proposal process works, the users who are involved in it and what features get proposed, approved, rejected or abandoned. To further engage with existing literature on the impact of diversity in OSM, we expanded these datasets in two ways: first, implementing an automated and reproducible process based on existing literature[20], we classified the proposals into \u201cfeminised\u201d, \u201cmasculinised\u201d and \u201cother\u201d (i.e., in neither category); second, we followed a semi-automated process to identify users\u2019 metadata about diversity, such as gender, language, and location.\nOur initial findings depict a landscape where 998 distinct users (this is, less than 0.01% of the total OSM user base) initiated a tagging proposal \u2013 a time-consuming (and mostly individual) endeavour of which only y 27.3% ever reach a decision. On a more positive note, against high abandonment rates, those proposals that do get voted on 73.2% are approved. We contend that because of the challenges of this system, most users only engage with the process of creating a proposal once in their lifetime. However, we identified a reduced subset of 59 users who are extremely prolific and have higher approval rates than other users. Their joint contributions account for 33.8% of the total proposals in the history of OSM, and 44% of the total approved proposals. Furthermore, we find that this group is quite homogenous in terms of gender, geography, and experience: it is heavily dominated by males from Europe and North America with white names and a long history in OSM.\nWe identified that feminised proposals are significantly less in numbers and systematically receive fewer number of votes than the others (which often leads to not being approved because of not passing the threshold), and when they do receive enough attention, they mainly receive opposing votes.\nThis research brings a new lens to existing literature on gender gaps in OSM community. The case of tagging proposals is particularly significant because it represents a higher level of participation over map contributions, as these decision-making processes directly shape OSM\u2019s ontology, and thus, the scope of the project. While our findings are in line with prior research, we highlight that problems flagged a decade ago not only are still present but become more accentuated as we climb the participation ladder[21]. Our paper, therefore, puts the focus back on how technology itself -regardless of how well-designed, flexible and well-intended it may be, as is the case of OSM- is not enough to address inequity and power structures. Beyond a description of the problems, we critically engage with the observed phenomenon and, ultimately, propose a series of recommendations in OSM\u2019s governance to make it as inclusive as it aspires to be.", "recording_license": "", "do_not_record": false, "persons": [{"code": "VRNBRT", "name": "Carlos C\u00e1mara", "avatar": null, "biography": "Carlos C\u00e1mara is a Senior Research Software Engineer at the [Centre for Interdisciplinary Methodologies](https://warwick.ac.uk/cim) at the University of Warwick (UK). He is an interdisciplinary researcher, with a background in architecture and urban sociology, whose research articulates around how physical and digital infrastructures, especially those that are commonly produced, respond to and are shaped by societal challenges. Currently, he is researching, inquiring and surfacing the world-views that are invariably embedded in technical artefacts such as platforms and maps, and how this affects under-represented communities. To do, he uses mixed-methods, with a preference for participatory approaches, digital methods and data visualisation. In his free time, he loves contributing to OpenStreetMap, and he has co-organised several humanitarian mapathons and accessibility mapping parties.", "public_name": "Carlos C\u00e1mara", "guid": "30b0200d-0f21-577f-9221-2b5d818c68d2", "url": "https://pretalx.com/sotm2026-osm-science/speaker/VRNBRT/"}], "links": [{"title": "Slides", "url": "https://doi.org/10.5281/zenodo.22158699", "type": "related"}], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/FLYV3H/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/FLYV3H/", "attachments": []}, {"guid": "db840178-9bc7-59e1-bf7b-a2173efddcd7", "code": "MWCJFD", "id": 98142, "logo": null, "date": "2026-08-29T17:20:00+02:00", "start": "17:20", "end": "2026-08-29T17:55:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-98142-consumed-at-scale-ai-driven-extraction-and-the-political-economy-of-openstreetmap", "url": "https://pretalx.com/sotm2026-osm-science/talk/MWCJFD/", "title": "Consumed at Scale: AI-Driven Extraction and the Political Economy of OpenStreetMap", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Large-scale scraping attacks on OpenStreetMap's infrastructure have exposed a fundamental tension between open geodata as a community-maintained commons and its role in current commercial AI development dynamics. This presentation examines the implications of AI-driven data extraction for OSM\u2019s infrastructure and licence compliance guidelines as well as its consequences for OSM\u2019s position within the geospatial data ecosystem.", "description": "Since its founding in 2004, OSM has navigated recurring tensions between voluntary contribution and corporate participation [1], however, the recent surge in AI-driven large-scale scraping of OSM infrastructure represents a qualitative escalation of this dynamic. In late January 2026, an OSM-affiliated account publicly appealed to journalists to investigate coordinated scraping activity; according to the responsible engineer Grant Slater, a single week saw 100,000 IP addresses querying OSM servers simultaneously, each making only a few requests [2, 3]. The OSM case is not isolated: in February 2026, a volunteer-maintained mapping project called Vaguely Rude Places saw daily requests jump from the low thousands to the hundreds of thousands, exhausting its monthly tile allocation in a single day and forcing the map offline. This was not due to technical failure, but because AI crawler demand had crossed a cost threshold the system was never designed to accommodate [4, 5]. These incidents illustrate how AI-/automated scraping translates directly into financial and operational costs for projects that were built around human-scale usage patterns.\n\nOSM's appeal for AI is substantial and growing. The dataset combines near-global coverage, fine-grained semantic tagging (place types, road classifications, building attributes, land use categories), a rich versioned edit history, and free availability under an open licence. For general-purpose language and multimodal models, OSM data can ground spatial analysis by linking named places to structured geographic attributes. Recent research has demonstrated a 70% improvement in geospatial prediction performance on certain tasks when LLMs are augmented with OSM auxiliary data [6]. For specialised geospatial AI (GeoAI), OSM geometries can serve directly as labelled training data for object detection, routing, and location intelligence systems, with documented applications in urban planning, hazard prevention, climate modelling, and traffic monitoring. OSM, in short, functions as a uniquely accessible and semantically rich proxy for human spatial knowledge and is increasingly being treated as such by commercial AI developers, without the community's consent and without meaningful reciprocity. \n\nThe precise mechanisms behind these access patterns remain unclear. OSM's infrastructure was built to meet the behavioural patterns of human users; AI-driven consumption accelerates and changes these patterns in ways the infrastructure was not designed to absorb [5]. Slater has described OSM as being \u201cat constant war\u201d with scrapers, noting that traffic frequently arrives through residential proxy networks that make it \u201chard to distinguish normal users from automated collection\u201d [7]. While the origins and intentions behind these access patterns remain largely opaque, they may in part be attributed to user queries directed at AI chatbots and assistants that generate maps or location-based outputs by drawing on openly accessible geodata in real time, a question that is increasingly discussed within online open-source communities and academia.\n\nThis paper examines the implications of AI-driven data extraction for OSM as an open-source project, focusing on two interrelated dimensions: infrastructural strain and licence compliance. It situates these dynamics within the broader political economy of open data and AI development, and discusses emerging governance concerns at the community, institutional, and regulatory level. We draw on a combination of document and content analysis, and reviews of relevant technical and legal literature. Primary sources include OSM Foundation communications, community forum discussions, and reports from the 2026 scraping events. These are contextualised through secondary literature on geodata and its intersection with digital commons [8, 9, 10, 1], and digital capitalism [11, 12]. The analysis is qualitative and interpretive, situated within a critical geography and science and technology studies (STS) framework [13], and deliberately foregrounds the political-economic and governance dimensions of these dynamics [14].\n\nTwo central dimensions structure our contribution. First, on the infrastructural effects: coordinated scraping exploits API infrastructure built and maintained largely by volunteers, generating financial and labour costs that fall disproportionately on the community. When volunteer capacity is consumed by bot-detection and infrastructure triage, the risk of contributor burnout increases. Simultaneously, the origins of these requests are structurally difficult to determine, as the technical mechanisms applied render automated and human traffic effectively indistinguishable.\n\nSecond, on licence compliance: the Open Database License (ODbL) governing OSM data imposes conditions on derivative databases, including requirements for open publication of training datasets constituting substantial extractions and for attribution in model documentation [15]. Whether these obligations are systematically met by commercial AI developers remains largely unverified and unenforced at this point. Notably, the OSMF's own attribution guidelines acknowledge that sophisticated models can be \u201covertrained\u201d to the point of recreating their training set, in which case the model output itself may constitute a derivative database subject to ODbL terms. \n\nTaken together, these dynamics point to a qualitative shift in OSM\u2019s position within the geospatial data ecosystem: from a collaborative infrastructure for mapping the world to an involuntary resource for AI systems that seek to model it. This shift reflects a deeper tension between the logic of open data, which presupposes a community of reciprocal contributors, and the logic of commercial AI development, which tends to treat open data as an input to be optimised rather than a commons to be maintained. The resulting risk is not enclosure in the classical sense of a commons being directly fenced off or privatised by external actors. Rather, it lies in a more paradoxical dynamic: large-scale, largely unaccountable extraction by commercial AI systems may push open mapping communities towards defensive forms of closure, such as more restrictive access controls, authentication requirements, or gated data infrastructures. Such protective enclosure would be a response to appropriation rather than its initial form, but it could nevertheless alter OSM\u2019s character as a digital commons and open-source project. We therefore identify these dynamics as a new and qualitatively distinct form of commercial pressure on OSM \u2014 one that operates not through direct participation in the project, but through large-scale consumption of its resources \u2014 and raise questions about how community-maintained infrastructures can adapt to AI-driven consumption patterns, how enforceable attribution mechanisms for OSM-derived data might be developed, and what meaningful reciprocity between commercial AI companies and the open mapping community could look like in practice.", "recording_license": "", "do_not_record": false, "persons": [{"code": "GTSVBC", "name": "Hannah Boettcher", "avatar": "https://pretalx.com/media/avatars/JMQEK9_iOhs7Ur.webp", "biography": "Hannah Boettcher is a doctoral researcher and research associate at the Institute of Geography at Friedrich-Alexander-University Erlangen-N\u00fcrnberg. Their research is situated in the field of digital geography, combining political- and social-geographical perspectives on digital spatial data with computational and qualitative methods. Hannah Boettcher is a founding and editorial member of the journal GRID (https://gridisnotajournal.de/), and their work includes social- and political-geographical research on OpenStreetMap as well as the social-scientific and discourse-analytical use of GIS-based methods. Methodologically, they combine qualitative approaches with computational text and data analysis, including natural language processing (NLP) and topic modelling, in order to investigate how digital spatial data and technologies reshape knowledge production and spatial governance.", "public_name": "Hannah Boettcher", "guid": "a5b75c32-fb9c-5060-a015-3c24a74e1afd", "url": "https://pretalx.com/sotm2026-osm-science/speaker/GTSVBC/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/MWCJFD/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/MWCJFD/", "attachments": []}]}}, {"index": 3, "date": "2026-08-30", "day_start": "2026-08-30T04:00:00+02:00", "day_end": "2026-08-31T03:59:00+02:00", "rooms": {"Martinique": [{"guid": "816224df-8a31-5597-a2b3-84a960284cfd", "code": "JC87RR", "id": 98021, "logo": null, "date": "2026-08-30T09:30:00+02:00", "start": "09:30", "end": "2026-08-30T10:05:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-98021-corporate-editing-and-collective-intelligence-in-openstreetmap-a-long-term-analysis-of-southeast-asian-case-studies", "url": "https://pretalx.com/sotm2026-osm-science/talk/JC87RR/", "title": "Corporate Editing and Collective Intelligence in OpenStreetMap: A Long-Term Analysis of Southeast Asian Case Studies", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This abstract examines how corporate editing affects OpenStreetMap\u2019s collective intelligence. Using OSM data from 2015\u20132025 for several countries in Southeast Asia, it operationalizes collective intelligence through measures of completeness, semantic richness, geometric complexity, diversity, innovation, and evenness, combined into a composite score. Descriptive country-level analysis and local-projection models for Thailand and Malaysia show that corporate editing has real but bounded effects: it often improves innovation and completeness, but some waves reduce decentralization. Effects fade within about two years, suggesting OSM\u2019s resilience.", "description": "Corporate editing (CE) \u2014 the large-scale mapping activity of paid teams employed by technology companies such as Meta, Grab, and Kaart \u2014 has become a prominent mode of OSM data production, deploying semi-automated and AI-assisted workflows at scales that far exceed typical volunteer contributions [1,2]. Concerns have been raised about CE's long-term consequences for the project's volunteer-driven character, contributor diversity, and data quality [3,4], yet empirical evidence remains fragmented, focusing primarily on single outcomes [5] or short observation windows. A systematic, multi-dimensional evaluation of CE's long-term impacts on OSM's collective production dynamics is lacking.\nWe address this gap through the Collective Intelligence (CI) framework. Previous work has already shown the value of analysing OSM-related mapping systems as collective intelligence systems, where contributor composition, collective action, and evidence of intelligent outcomes must be considered together rather than treated as separate dimensions of data production [6]. CI is contingent on four enabling conditions: independence of contributors, diversity of opinion, decentralization of decision-making, and an effective aggregation mechanism [7]. These conditions correspond directly to the tensions raised by CE: scale-intensive contributions may enhance aggregation while simultaneously reducing independence or diversity. Framing the analysis in CI allows us to interpret simultaneously improving and deteriorating dimensions, and ask whether CE sustains, erodes, or transforms the collective production dynamics that make OSM valuable as shared geographic infrastructure.\nWe operationalize CI using six measures derived from the OSM data model, applied to road (highway=*) and building (building=*) features. Three capture data quality as an aggregation outcome: entity count (coverage and completeness), mean tag richness per entity (semantic content, restricted to tags within the top 25th percentile of global co-occurrence frequency to exclude noise), and building geometric complexity (1 minus the ratio of footprint area to convex hull area, reflecting spatial detail). Two capture diversity and innovation: the Shannon diversity index over tag distributions and unique tag counts (distinct tag keys per entity type per spatial unit). One captures independence and decentralization: Shannon evenness of tag distributions, where low values indicate convergence toward homogeneous mapping practices. These yield 11 individual measures, combined into a composite CI score via the EWM-TOPSIS objective weighting approach.\nThe framework covers five Southeast Asian countries \u2014 Thailand (TH), Malaysia (MY), Myanmar, the Philippines, and Papua New Guinea \u2014 over 2015\u20132025. Annual observations represent the OSM database state on 1 January of each year, extracted via the OHSOME API [8]. CE changesets were identified from the OSM API using hashtags documented on organized editing Wiki pages for the three most active corporations in the region: Meta/Facebook (#nsroadimport, #mapwithai), Grab (#grab, #grabosm), and Kaart (#kaart). TH and MY \u2014 the focal countries of the Facebook AI-Assisted Road Tracing project [9] \u2014 serve as primary case studies; the remaining three countries provide comparison contexts at the country level only.\nCountry-level analysis is descriptive. Province- and state-level causal modeling for TH (77 provinces) and MY (16 states) uses Jord\u00e0-style local projections (LP) at horizons h = 0, \u2026, 4 years, with log(1 + CE changesets) as the treatment variable, region and year two-way fixed effects, and lagged outcome and treatment terms. Two CE waves are identified per country \u2014 Wave 1 (Facebook/Meta: 2018\u20132019 in TH, 2019\u20132020 in MY) and Wave 2 (Grab/Kaart: 2024\u20132025 in both) \u2014 and estimated via interaction terms marking deviations from the baseline treatment slope. A pooled model adds country \u00d7 period indicators to prevent country-specific phase shifts from driving pooled estimates. For TH, spatial spillover effects are modeled by adding spatially lagged CE exposure under k-nearest neighbors and inverse-distance weight matrices. \nCountry-level time series results (Figure 1) show that CE-intensive countries do not follow trajectories meaningfully distinct from comparison countries. The Philippines \u2014 driven by an active local community with minimal corporate involvement \u2014 consistently outperforms all others on the composite CI score, pointing to community-driven mapping as a viable and potentially more effective pathway to collective intelligence.\nProvince-level analysis for TH (Figure 2) overturns several country-level findings \u2014 notably, highway evenness recovers to pre-Wave 1 levels during Wave 2 \u2014 and exposes strong temporal autocorrelation that motivates causal modeling. State-level results for MY follow a similar pattern, identifying the first wave as a turning point for several highway measures.\nLP results are presented as heatmaps with rows corresponding to CI measures and columns to horizons h = 0\u20134 (Figure 3). Three panels are shown side by side: baseline CE effects (\u03b2h), Wave 1 interaction (\u03b2h,w1), and Wave 2 interaction (\u03b2h,w2). Warm colors indicate positive coefficients, cool colors negative; asterisks mark significance levels (* p < 0.1, + p < 0.05, \u2020 p < 0.01). Significant baseline effects are predominantly positive, concentrated on innovation (unique highway tag counts) and completeness, and are fully absorbed by h = 3. Wave 1 attenuates these effects and reduces decentralization (negative building evenness coefficients persisting to h = 2); Wave 2 amplifies positive effects across most highway measures. Results for MY replicate the wave contrast but differ in significance patterns and coefficient signs, illustrating context dependence. Spatial spillover effects are limited and rarely reach statistical significance.\nThese findings carry several implications. CE's impacts are real but bounded: absorption within approximately two years suggests OSM functions as a resilient self-correcting system. The wave contrast is consistent with a learning trajectory in which corporate teams progressively align with community norms \u2014 a process best accelerated through deeper integration into OSM's community structures. The superior CI performance of the Philippines relative to CE-intensive countries reinforces the value of community-driven contributions. The risk of corporate disengagement, illustrated by the establishment of the Overture Maps Foundation [10] and a concurrent decline in CE activity [4], underscores the importance of institutional structures that reduce OSM's dependence on any single contribution mode. Intra-OSM platforms for cross-team knowledge exchange would reduce the repetition of harmful practices and help channel corporate resources \u2014 career pathways, technical infrastructure, and mentorship capacity \u2014 toward contributor retention [11], one of OSM's central sustainability challenges.\nThe codebase underlying this analysis is currently being prepared for open publication and will be made fully available under an open source license in time for the conference.", "recording_license": "", "do_not_record": false, "persons": [{"code": "9VQ9VC", "name": "Yair Grinberger", "avatar": "https://pretalx.com/media/avatars/3UBLLA_M9YOOLd.webp", "biography": "Dr. Yair Grinberger is a senior lecturer of geoinformatics at the Department of Spatial Science at the Hebrew University of Jerusalem. His main interests are Geographical Information Science, OpenStreetMap Science, geo-cultural analysis, urban agent-based modeling, and mobility analysis.", "public_name": "Yair Grinberger", "guid": "9a9c1f4c-554f-5a44-90dd-91222bc44914", "url": "https://pretalx.com/sotm2026-osm-science/speaker/9VQ9VC/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/JC87RR/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/JC87RR/", "attachments": [{"title": "Slides - PDF", "url": "/media/sotm2026-osm-science/submissions/JC87RR/resources/CE_im_82cxGTk.pdf", "type": "related"}]}, {"guid": "bde12b82-5f17-5b0e-9df1-b24134ea8c37", "code": "3PBPMG", "id": 96911, "logo": null, "date": "2026-08-30T10:05:00+02:00", "start": "10:05", "end": "2026-08-30T10:40:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-96911-detection-and-semantic-annotation-of-changes-in-openstreetmap-data", "url": "https://pretalx.com/sotm2026-osm-science/talk/3PBPMG/", "title": "Detection and Semantic Annotation of Changes in OpenStreetMap Data", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Geographical databases such as OpenStreetMap (OSM) are continuously evolving to reflect ground-level transformations. Identifying and characterizing these changes is a key challenge for analyzing evolution over time. While several works have analyzed OSM edit history, the semantic interpretation of changes and the ability to distinguish genuine ground-level evolution from data refinements, tagging corrections, or modeling decisions, remains largely unaddressed. For planners relying on OSM, distinguishing real infrastructure changes from editing artifacts is critical. We address this challenge through two contributions: a web application to explore change dynamics and a methodology for semantic annotation and interpretation of edits.", "description": "Geographical databases such as OpenStreetMap (OSM) are continuously evolving to reflect ground-level transformations. Identifying, describing, and characterizing these changes is a key challenge for analyzing territorial evolution over time. While several works have analyzed OSM edit history for quality assessment [1] or contributor behavior studies [2], the semantic interpretation of changes and, in particular, the ability to distinguish genuine ground-level evolution from data refinements, tagging corrections, or modeling decisions, remains largely unaddressed. For planners relying on OSM, distinguishing real infrastructure changes from editing artifacts is critical. We address this challenge through two contributions: a web application to explore change dynamics and a methodology for semantic annotation and interpretation of edits.\n\nOur data source is the Planet file with history, which encodes every version of every object ever created in OSM since 2007 [3]. From this corpus, we use the ohsome-planet tool [4] to extract the complete edit histories of three study cities (Lisbon, Grenoble, and Paris) covering the full OSM timeline. The methodology is currently instantiated on cycling infrastructure, enabling the study of whether and how cities are progressively transitioning toward soft mobility, by tracking how cycling infrastructure is documented and refined in OSM over time. It is designed for generalization to any OSM object family.\nWe identified all tags related to cycling over the study period and selected a subset based on their frequency and their consistency with established OSM tagging practices. This curated set forms the basis for tracking tag usage and its evolution over time. These tags are then grouped into four semantic categories reflecting the main families of cycling infrastructure: Dedicated cycleways (e.g., highway=cycleway) Shared-use paths (e.g., highway=path + bicycle=designated), On-road lanes (e.g., cycleway=lane), and Designated routes (e.g., oneway:bicycle=no). Within this framework, we also distinguish two tiers of OSM tags: primary attributes, whose values determine which cycling infrastructure category an object belongs to, and secondary attributes, which capture additional properties of the object (e.g., street name, surface type, width). This distinction drives the interpretative pipeline: transitions at the main attribute level are considered significant events and are the primary focus of our analysis, as they capture the most meaningful shifts in how cycling infrastructure is represented and potentially built. Changes to secondary attributes provide complementary context for interpreting these dynamics.\n\nThe pipeline operates across two levels of analysis: (1) the observable level and (2) the interpretative level. At the observable level (1), changes between successive feature versions are conceptualized as transitions and classified along five dimensions: existence (creation, deletion), category, attribute (tag addition, modification, removal), geometry (transformation (e.g., node-to-way), geometry modification, densification, sparsification), and structure (fragmentation, aggregation, recomposition). This ontology provides a fine-grained, machine-readable description of what changes in OSM data over time.\nThis observable level is fully implemented and constitutes the current operational output of the pipeline. However, isolated transitions are inherently ambiguous. For instance, a tag change may reflect a correction, a refinement, or a real-world modification. To address this limitation, we specify an interpretative level \u2013 currently under implementation \u2013 that operates on sets of transitions rather than individual ones, aggregated along two axes. The first axe is Co-occurrence: transitions of the same type are grouped together when they appear massively across the study area within a given period. The second is Coherence: transitions of different types may be grouped when they share other characteristics, namely temporal coherence (simultaneous or close in time), spatial coherence (geographic clustering), contributor coherence (same OSM author or same changeset), or structural coherence (connected objects).\n\nInterpretations are formalized through an ontology structured around four high-level categories: Representation refinement (semantic, attribute, or geometric updates that improve how features are modeled without correcting an error); Error Correction (fixes to incorrect category, attribute, or geometric information); real-world evolution (genuine infrastructure creations, deletions, or modifications); and ambiguous cases (transitions that cannot be interpreted with certainty). Inference rules map sets of observable transitions to interpretative categories, acknowledging that a single transition may admit multiple interpretations. Rules are organized by target interpretative category. For instance, for Representation refinement: IF the same tag modification occurs on a large number of features within a short time window AND across a wide spatial extent \u2192 THEN semantic refinement. And, for Correction: IF a feature is deleted AND a semantically equivalent feature is created nearby within the same changeset or time window \u2192 THEN correction by replacement. \n\nThe outputs of this pipeline are published as a Knowledge Graph (KG) following the Change Bridges approach introduced by the TSN-Change ontology [5]. Rather than representing OSM features themselves as a KG, as done in projects such as WorldKG [6], our KG exclusively describes the detected changes, linking each change node to the stable URIs of the corresponding OSM features. This design choice keeps the KG lightweight and focused on temporal dynamics, while remaining interoperable with existing geographic KGs.\n\nAlongside the pipeline, we developed a dedicated web application to explore OSM editing trends over time and visually verify results by inspecting the lifecycle of selected changing features. The application operates directly on data derived from the full history planet file. At present, the application and the KG are two independent components. The former focuses on data exploration, the latter on change description and interpretation. However, the two are designed to converge: in future work, change annotations produced by the pipeline will be surfaced directly within the application's timeline, enabling users to interpret editing trends in light of their semantic classification and to better distinguish real-world evolution from data refinements.\nThe interface of the exploratory application is composed of various components: a geographic map displaying the cycling network at different points in time; interactive timelines showing the evolution of tag usage and frequencies over the full OSM history; an extended data analysis panel, including individual tag counts for each point in time, evolution of individual tags across time, tag transition counts and a Sankey diagram encoding the flow of semantic transitions between the defined categories (e.g., from \u201cshared use path\u201d to \u201cdedicated cycleway\u201d) across time. The map and the timeline are synchronized: selecting a time range on the timeline simultaneously filters the map. Figure 1 illustrates the general layout of the application and the synchronized map-timeline interface, while Figure 2 shows a Sankey diagram capturing the dominant transition flows within the cycling network of Paris.\n\nApplied to the cycling network across the three study areas, the pipeline currently produces a KG encoding the detected and classified transitions at the observable level, covering the full OSM history of Lisbon, Grenoble, and Paris.  The interpretative level is fully specified and under active implementation. Together, these contributions lay the groundwork for a robust and extensible framework for OSM change interpretation, with direct applications in urban monitoring and data quality assessment. Finally, as we are committed to an open science approach, we will publish the application\u2019s code as open source and immerse the knowledge graphs into the Web of Data so that they are accessible to anyone wishing to explore them.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EB9VU7", "name": "Camille Bernard", "avatar": "https://pretalx.com/media/avatars/VTBAUP_wJgoxpg.webp", "biography": "Associate Professor in Computer science at Grenoble INP, Univ. Grenoble Alpes, France. \nMy research focuses on representing the evolution of data over time within the Web of distributed data, also known as the Linked Open Data (LOD) Cloud or Semantic Web.", "public_name": "Camille Bernard", "guid": "6a241e02-cd77-5731-925a-af9d39ac1afa", "url": "https://pretalx.com/sotm2026-osm-science/speaker/EB9VU7/"}, {"code": "QERHUN", "name": "PedroPinheiro", "avatar": null, "biography": "MSc 2nd year Intern at LIG", "public_name": "PedroPinheiro", "guid": "31bbc90c-b3b3-57b8-a939-4e48e405d3cd", "url": "https://pretalx.com/sotm2026-osm-science/speaker/QERHUN/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/3PBPMG/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/3PBPMG/", "attachments": [{"title": "PDF Presentation", "url": "/media/sotm2026-osm-science/submissions/3PBPMG/resources/OSM-S_c073vzV.pdf", "type": "related"}]}, {"guid": "f610225e-30cc-5757-9fe1-4ea79e92ec8d", "code": "KYSVEE", "id": 98079, "logo": null, "date": "2026-08-30T11:15:00+02:00", "start": "11:15", "end": "2026-08-30T11:50:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-98079-assessing-data-completeness-in-openstreetmap-using-the-geometrical-and-temporal-properties-of-mapping-patterns", "url": "https://pretalx.com/sotm2026-osm-science/talk/KYSVEE/", "title": "Assessing Data Completeness in OpenStreetMap using the Geometrical and Temporal Properties of Mapping Patterns", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This study introduces a novel intrinsic completeness metric for OpenStreetMap data, based on the temporal evolution of mean feature geometry, requiring only aggregate feature statistics and no external reference. The results currently depict an exploratory phase and show the method to be robust, computationally simple and potentially universally applicable, albeit still restrictive.", "description": "Since the launch of OpenStreetMap (OSM), the quality of its data has been a prominent study subject. Among the various aspects of data quality, data completeness \u2013 i.e., whether all real-world geographic features in a given extent exist in the dataset \u2013 remains a central element and a prominent challenge [1]. Existing approaches to assessing the completeness of data in OSM can be broadly grouped into three main groups: extrinsic comparison against a reference dataset [2-5]; intrinsic analysis of some proxy variable in the data [6-8]; and statistical methods that include curve fitting and Deep Learning algorithms (DL) [9-12]. \nExtrinsic analysis, though the best estimator, requires an authoritative dataset, thus limiting this approach\u2019s applicability in regions where official or proprietary data are unavailable, outdated, or costly. Intrinsic methods compare OSM's edit history (e.g. edit frequency, contributor counts) with human development proxies (such as night lights or GDP) and allow for better scalability and flexibility between regions but tend to reflect local activity more than mapping outcomes. Statistical modeling approaches, such as fitting Sigmoidal curves to cumulative road lengths [10] and applying DL to detect missing features [11], require good training data and may not translate well over regions of different characteristics.\nThis study presents a novel intrinsic completeness metric based on the evolution of mean feature geometry, that requires only aggregate feature statistics and no external source. The method uses cumulative time series data, but differs from prior studies, e.g. [7, 11], by focusing on the average relative change in the cumulative value, instead of the absolute value, accounting for both feature additions and geometry alterations. The underlaying assumption draws on the works by Corcoran et al. [13] and Strano et al. [14], showing that road networks are typically mapped in a process in which exploration is inherited by densification. In other words, contributors initially add the largest, most prominent features (major roads, large and central buildings) and gradually move on to smaller and more detailed elements (minor roads, small structures). Thus, we can expect the average size (area\\length) per added feature to decrease over time, until it converges to some general threshold such that the data can be considered saturated (or \"complete enough\" for usage). Simply put, we expect to see the temporal signature of the average feature size per new addition (denoted aa_t) to stabilize over time. For comparative analysis between regions, the metric is normalized to the largest average addition in each time series (denoted naa_t). \nA complete region can be identified using this method if the series reaches a defined stable saturation period, meaning naa_t<\u03b1 for at least \u03b2 years, \u03b1 and \u03b2 being pre-defined thresholds. This creates a robust method that integrates both a standard adjustable threshold that accounts for different mapping dynamics and enables a completeness metric of different mapped elements via their geometric attributes. To make the method more robust, we define two subsequent calibration tests in relation to the saturation period: testing for an external disruption that may have altered the signal periodically (e.g. a data export or HOT mapping party) and bounding the relative growth, i.e. the relative addition in the saturation period, against another threshold \u03b3. These tests are defined to detect false positives.\nAs an initial exploratory assessment of its behavior, we implement the method for a sample of 446 regions categorized by distinct mapping dynamics [15], allowing us to test the method\u2019s sensitivity to large scale events. Aggregated mapping pattern statistics, i.e. total number and cumulative length of roads (way features, highway=* tag) and total number and cumulative area of buildings (way features, building=*) per month, were extracted using the Ohsome API [16]. Our implementation sets \u03b1=0.1,\u03b2=2 and \u03b3=1.5 as threshold values, meaning all the relative additions were less than 10% from the highest addition for a period of 2 years or longer, bound to less than 50% relative addition in said period.\nInitial results are promising though somewhat restrictive, with 17% of all regions labeled as complete for building features and 8% for roads, meaning it can be acute to different feature types' mapping trend. These results were analyzed through two validation tests \u2013 analyzing completeness in relation to feature density and analyzing a randomly selected set of locations - and a statistical analysis of the sampled region's attributes. Test 1 found that both complete and incomplete regions displayed similar density distributions, suggesting robustness to both rural and urban areas. Test 2 showed that even for a randomly selected group of regions and cities from around the world, the sensitivity to different feature types holds. The statistical analysis results show that some complete regions had a very long stability period despite having a consistent relative addition. However, in both the original sampled data and the random control group in test 2 a dominant reason for incompleteness was the unbounded relative addition, meaning saturation periods were long enough to typically result in a consistent addition that fails the calibration test, perhaps due to an ill representation of the data (see figure A). This marks the method restrictive, albeit robust. Finally, analyzing completeness frequency by mapping dynamics and spatial extents showed both distributions to maintain a relatively uniform shape, implying the method robust to both attributes (see figure B).\nThe study contributes to OSM Science in several ways. First, it introduces the geometric properties of mapped features \u2014 specifically, their average added size \u2014 as a completeness signal, a dimension absent from existing literature. The method can be implemented for various way feature types and applied for different confidence intervals (using the \u03b1,\u03b2 and \u03b3 thresholds), allowing a robust and flexible completeness assessment. Second, the metric's data requirements are exceptionally lightweight: it needs only aggregate statistics (feature count and total length or area) at regular time intervals, with no extrinsic datasets or further intrinsic attributes. Moreover, it seems to be indifferent to case study attributes. This makes it computationally inexpensive and universally applicable in principle. The results presented here are only exploratory, meant to understand how this method behaves. Next steps will focus on validating and refining it further, using extrinsic data sets such as Overture maps and a building dataset mapped by the Survey of Israel. Furthermore, the limitations of the metric, mainly the restrictiveness of the method, will be investigated in greater depth.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BZLNEN", "name": "Eliya Levin", "avatar": null, "biography": "Graduate student in Geoinformatics at the Hebrew University of Jerusalem, interested in Spatial Data Science and the applications of Volunteered Geographic Information (VGI) and GeoAI for urban planning and spatial modeling. Member of the Geo-Cultural Information Lab, researching quality measures and the effect of corporate contributions to OSM.", "public_name": "Eliya Levin", "guid": "66e3bfd7-8ff8-5ec9-a3c6-13aea24c06c2", "url": "https://pretalx.com/sotm2026-osm-science/speaker/BZLNEN/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/KYSVEE/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/KYSVEE/", "attachments": []}, {"guid": "8fd2010a-6cc0-5759-87d0-8be042065455", "code": "7HWF8H", "id": 98138, "logo": null, "date": "2026-08-30T11:50:00+02:00", "start": "11:50", "end": "2026-08-30T12:25:00+02:00", "duration": "00:35", "room": "Martinique", "slug": "sotm2026-osm-science-98138-global-mining-activity-in-openstreetmap-methodological-challenges-and-data-quality-analysis", "url": "https://pretalx.com/sotm2026-osm-science/talk/7HWF8H/", "title": "Global Mining Activity in OpenStreetMap: Methodological Challenges and Data Quality Analysis", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This study evaluates OpenStreetMap's reliability for global mining mapping, revealing significant data gaps and classification errors compared to scientific benchmarks. It highlights the need for refined filtering methods and better contributor practices to overcome these geographic and semantic biases.", "description": "The OpenStreetMap (OSM) database offers a wealth of collaborative geographic data, providing diverse thematic perspectives on a global scale. This study focuses specifically on global mining activity, a domain of critical economic, environmental, and societal importance. By applying an ad hoc GIS methodology, we generated an initial data layer featuring one point per potential mining site.\n\nOur approach began with selecting specific OSM tags related to mining. In addition to community-recommended tags (via the OSM Wiki), we utilized the Taginfo API to identify and include additional relevant tags, particularly those identifying ores extracted through mining activities. Data extraction was performed using the Overpass Turbo API, yielding a total of 29,233 polygon entities, 13,630 lines, and 58,628 points, within 3 different layers. Many sites extracted using the rules described above include tags indicating their heritage status and/or abandonment. It is therefore necessary to exclude them from our database. We then applied a rigorous filtering process to exclude historical sites (e.g., destroyed, abandoned, or in ruins) and tourist attractions using a large SQL query with more than 200 arguments.\n\nTo further refine the dataset, we implemented a shape-based filter to detect \"atypical\" and complex geometries unrelated to mining activity. For example, in Florida, entities tagged as man_made=spoil_heap appear to be linked to canals and may correspond to embankments. We employed Miller\u2019s Circularity Ratio [1] as a compactness index. Finally, the layers were simplified using spatial selections; centroids were calculated for polygons and lines, and the layers were then aggregated to produce a single layer where each potential site is represented by a unique point. Lastly, any duplicates were removed.\n\nThis process yielded a dataset of 39,291 points. For validation, we compared our results with two benchmark international studies: Maus et al. (2022) [2], which identified 44,929 sites, and Tang and Werner (2023) [3], with 74,548 sites. These studies rely on satellite image interpretation.\n\nA visual analysis reveals that OSM\u2019s spatial distribution differs significantly from these benchmarks \u2013 which themselves exhibit notable discrepancies. OSM data appears relatively consistent across the Americas but is overrepresented in Europe and largely underrepresented throughout the rest of the world. Furthermore, spatial correlation is low: only 7.2% of OSM sites fall within Tang and Werner\u2019s (2023) polygons, and 8.9% within those of Maus et al. (2022). Notably, since the two benchmark studies only share a ~48% overlap, these results must be viewed within the context of general global mapping uncertainties.\n\nWhile the quantitative results are below expectations, they highlight critical areas for discussion. Our extraction yielded fewer sites than the benchmarks but also identified numerous sites absent from them. We propose four hypotheses for these discrepancies:\n- Insufficient completeness of OSM regarding mining themes.\n- Inconsistent data entry, where contributors use incorrect geometries or incomplete tagging.\n- Filtering inefficiencies inherent to the tags used in our methodology.\n- Unique OSM entries representing valid sites missed by other global studies.\n\nThese are typical biases. OSM is a quintessential example of Volunteered Geographic Information (VGI) [4] a nature that inherently raises questions regarding the quality of the provided data [5]. Indeed, the specific characteristics of such data can sometimes be difficult to grasp for the \u201ccitizen-sensor\u201d [4], who is often a non-specialist. Furthermore, one might surmise that the mining theme is particularly challenging to grasp in this context.\n\nSeveral factors significantly degrade the output quality:\n\n- The \u201cQuarry\u201d vs. \u201cMine\u201d Ambiguity: The quarry tag is frequently misused for mining operations. For example, 81% of entities tagged with resource=gold are classified as quarries, despite gold extraction being a mining activity. Furthermore, 68% of quarries (156,014 entities) lack any resource tag, making it difficult to distinguish them from actual mines.\n- The Historical Bias: In Europe, the overrepresentation of sites is largely due to abandoned or historical locations that lack the proper historical or disused status tags.\n- Semantic Misuse: We identified significant interpretation and translation errors. Examples include: 1. A French site named \u201cAncienne mine de\u201d (in English: \u201cFormer mine of...\u201d) tagged as man_made=adit but lacking historical indicators; 2. A historic wine cellar (cultural heritage) tagged solely as man_made=adit; 3. Caves documented by speleologists using the man_made=adit tag.\n\nDespite these challenges, OSM likely contains valid mining sites not captured by existing scientific inventories. Future improvements could involve using the name key to filter for adjectives indicating historical status (e.g., \u201cformer\u201d, \u201cabandoned\u201d, \u201cancient\u201d). However, this would require a complex multilingual approach to cover the 10 million+ OSM users.\n\nThis research underscores the need for better contributor practices and more comprehensive tagging. While these initial tests produced results below initial expectations, they established a functional, replicable GIS methodology. Automating this process will be the next step, allowing for iterative testing and refinement to improve the accuracy and efficiency of global mining mapping.", "recording_license": "", "do_not_record": false, "persons": [{"code": "8WPJED", "name": "Pierre-Olivier Mazagol", "avatar": "https://pretalx.com/media/avatars/LQK38N_4rZtoWt.webp", "biography": "Pierre-Olivier Mazagol holds a PhD in Geography and is a research engineer at the \"Environnement, Ville, Soci\u00e9t\u00e9\" laboratory (EVS, UMR 5600 CNRS) at Jean Monnet University, Saint-\u00c9tienne. His work focuses on geomatics techniques, the environment, and cultural heritage through an interdisciplinary approach.", "public_name": "Pierre-Olivier Mazagol", "guid": "130344fb-a9a3-59a7-8e33-2d438c122ae2", "url": "https://pretalx.com/sotm2026-osm-science/speaker/8WPJED/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/7HWF8H/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/7HWF8H/", "attachments": []}, {"guid": "afc8fb00-3ff7-5ff0-915f-e25c982c6b9c", "code": "EL3XUW", "id": 97971, "logo": null, "date": "2026-08-30T12:25:00+02:00", "start": "12:25", "end": "2026-08-30T12:30:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-97971-from-centroid-to-entrance-a-global-assessment-of-poi-access-locations-for-accessibility", "url": "https://pretalx.com/sotm2026-osm-science/talk/EL3XUW/", "title": "From centroid to entrance: a global assessment of POI access locations for accessibility", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Transport models routinely anchor points of interest (POIs) at the centroid of their building or parcel. On large multi-building sites, this can misrepresent pedestrian access, distorting first-/last-mile transit access, wheelchair routing, and curbside drop-off precision. We quantify how explicit `entrance=*` detail in OSM shifts accessibility-style metrics relative to centroid proxies. The study uses a global, population/built-volume-weighted random sample to compare centroids against the nearest main entrance. We release the full toolchain that persists measurements between POI / entrance / centroid anchors and the nearest road network and transit stop.", "description": "**Introduction**\n\nPoints of interest (POIs) and residential locations serve as origins and destinations when estimating travel demand and modelling behaviour. The underlying data come from travel surveys, property registries, GPS traces and, increasingly, OSM, which acts both as a crowdsourced database and as an openly licensed container for license-compatible authoritative datasets. When footprints are available, most researchers approximate each POI by the centroid of its polygon (or the parcel where no building is mapped). On small single-purpose buildings this is acceptable; on larger and multi-building sites the centroid can misrepresent the access point by hundreds of metres \u2014 sometimes kilometres \u2014 which is not negligible for walking or first-/last-mile transit.\n\nEntrances can be tagged explicitly via OSM `entrance=*`, but remain sparsely mapped outside a few well-covered regions. Large sites usually expose several entrances with distinct functions \u2014 main, visitors, employees, deliveries, emergency \u2014 each with its own accessibility profile. Precise access points matter most for paratransit and wheelchair users, transit riders, cyclists seeking secure parking, and routing engines including autonomous taxis, for which curbside drop-off precision is a safety and usability requirement.\n\nA growing literature characterises the completeness and positional accuracy of OSM buildings and POIs (Haklay, 2010; Girres & Touya, 2010; Fan et al., 2014; Herfort et al., 2023; Klinkhardt et al., 2023) and shows that entrance-level detail changes transit catchments (Guti\u00e9rrez & Garc\u00eda-Palomares, 2008; El-Geneidy et al., 2014), wheelchair routing (Neis & Zielstra, 2014), and curbside outcomes for autonomous vehicles (Hunter et al., 2024; Wang et al., 2025). The `entrance=*` tag itself remains a blind spot: Hu et al. (2021) found only \u224860 tagged buildings in their entire London extract. Our prior work (Bourbonnais et al., 2026) validated 1,034 POIs from five Qu\u00e9bec household travel surveys and observed median positional errors around 20 m across OSM, Overture Maps, the Qu\u00e9bec Property Registry and Google Places, with long tails dominated by university campuses and hospital complexes; it explicitly flagged the centroid-vs-entrance comparison as a future research question. To our knowledge, no study yet quantifies `entrance=*` coverage at scale, nor the cost of substituting a centroid for an entrance. We quantify that gap on a worldwide random sample of POIs by comparing centroid baselines to the nearest tagged entrance, and show how entrances can be mapped to support walking, cycling, transit and autonomous-vehicle use cases.\n\n**Methodology**\n\nThe study is a global exploratory analysis. POIs are coded along four axes: building footprint; single- vs. multi-building site; `entrance=*`; and specificity of the entrance tags. Coverage extends beyond buildings to polygon-mapped sites without a unique footprint \u2014 e.g. university campuses, large factory areas, or sport pitches \u2014 where the centroid is calculated on the total area.\n\n**Two complementary sampling strategies.** A global pass tessellates the world into 10\u00d710 km cells over population density using GHS-POP and built volume using GHS-BUILT-V (Joint Research Centre, European Commission) and draws cells with probability proportional to a *blended* mix of population and built volume (default \u03b1 = 0.5). The built-volume term prevents low-residency but high-activity fabric \u2014 campuses, industrial parks, transport hubs \u2014 from being under-sampled by a purely population-weighted draw. The current round is purely random, which under-samples the large multi-building sites where the centroid proxy is most misleading. A complementary targeted pass \u2014 the analyst supplies a precise point or OSM node/way/relation \u2014 will characterise the upper tail on a curated list of large sites; both passes share the same pipeline.\n\n**Reference entrances and centroid baselines.** Reference entrances are established from open and proprietary street-level imagery (Mapillary, KartaView, Panoramax, Google Street View, and the Chinese Baidu Panorama and AMap services) and, where available, architectural or wayfinding plans. A non-trivial share of randomly selected POIs are rejected at this stage \u2014 most often because aerial and street-level imagery do not let the analyst confidently locate entrances, or because the underlying tag is obsolete (e.g. a closed shop). Four centroid baselines are compared against each OSM entrance: parcel, main-building, and all-buildings centroids where a footprint exists, and the area-polygon centroid for sites without a unique footprint (campuses, factory areas, sport pitches).\n**Distance metrics and persisted measurements.** The analyst draws a polyline on the focus map \u2014 a single Euclidean segment, or a multi-vertex trace along sidewalks/desire lines where mapped paths are missing. Each polyline is persisted with its geodesic length, walking duration at a configurable speed, coordinates, an analyst-supplied *purpose* (nearest entrance, main entrance, transit stop, off-street parking, driving road, and walking/cycling/driving network combinations) and *entrance class* (`main`, `customers`, `home`, `emergency`, service-employees, service-delivery, `garage`, or one of four centroid baselines: parcel, main-building, all-buildings, or area).\n\n**Pipeline and OSM contributions.** The pipeline is a Rust backend over PostgreSQL/PostGIS, fed by Overpass for OSM features, with a React/MapLibre review interface; all code, the sampled POI list and computed metrics are released under an open licence. OSM edits made during the analysis to add missing entrances and pedestrian-access geometry (footpaths and connecting paths) for the selected POIs carry the `#StateOfTheMap2026` and `#EntranceAnalysis` hashtags in their changeset comments, so every contribution is traceable. Repository: https://github.com/chairemobilite/stateofthemap2026 (translations from french, drafting and parts of the pipeline were assisted by Anthropic Claude Opus 4.6 and 4.7; all data, methods and findings remain the authors').\n\n**Review interface**\n\nThe review service is a Rust/React/MapLibre application (Figure 1) with four screens. *Sampling* draws a candidate 10\u00d710 km cell or accepts a custom anchor. *Kept bboxes* lists accepted cells and their POI inventory. *POI focus* centres the map on a randomly selected POI, lets the analyst draw routing measurements over OSM centroids and entrances, and provides right-click deeplinks to the imagery providers and to the OSM iD editor. *Stats* aggregates min, max, mean and median length and walking duration over all persisted measurements.\n*Figure 1. POI focus screen for one POI: building polygons, entrance nodes (green), the configurable focus radius (orange), and an analyst-drawn polyline from the building centroid to the nearest tagged entrance.*\n\n**Preliminary findings**\n\nThe initial pass is a 100% random draw of \u224825 POIs, predominantly in North America (Figure 2), yielding 140 measurements. For each POI we pair the centroid-anchored polyline with the nearest tagged-entrance polyline drawn to the same target and report the per-POI difference; a positive value means the centroid proxy yields a longer walk than the real entrance.\n*Figure 2. Random initial sample: kept 10\u00d710 km cells worldwide (green: completed POI; orange: in progress; blue: not yet started).*\nAcross the 25 paired observations, 24 have longer walk from centroid to nearest road network than from entrance (single exception where the centroid is closer than the entrance). The pooled difference is +16 m median, +80 m max for distance to nearest road network (n = 15); +37 m median, +92 m max for distance to nearest transit stop (n = 10).\n**These are preliminary figures from a small random draw.** The full paper will combine a substantially larger random sample and a targeted pass on large multi-building and area-coded sites..", "recording_license": "", "do_not_record": false, "persons": [{"code": "LTTXCQ", "name": "Pierre-Leo Bourbonnais", "avatar": "https://pretalx.com/media/avatars/W3V7PH_YqAId1f.webp", "biography": "Pierre-L\u00e9o Bourbonnais is a Research Professional at Chaire Mobilit\u00e9, Polytechnique Montreal. He holds a Ph.D. in Civil Engineering and specializes in travel survey methodology, public transit network design and optimization, and geospatial data management. He is one of the lead developers of Transition, an open-source transit planning platform, and Evolution, a travel survey interview platform. As a member of the OpenStreetMap Foundation, he actively contributes to mapping and validating geospatial data across Quebec and Canada. His work bridges academic research with practical applications, with publications in leading transportation journals.", "public_name": "Pierre-Leo Bourbonnais", "guid": "07670283-40bb-5086-97d5-7004888e20e3", "url": "https://pretalx.com/sotm2026-osm-science/speaker/LTTXCQ/"}, {"code": "98TMMP", "name": "Yannick Brosseau", "avatar": null, "biography": "Yannick is a versatile computer engineer working mostly on open source projects. He did is first contribution to OpenStreetMap in 2010 and is an active member of the Montr\u00e9al community. He is a research professional at the Chaire Mobilite lab at Polytechnique Montreal writing for their open source transit planning platform Transition platform and working on their backend infrastructure. He has teached several course, including parallel programming and embedded software development using OSS tools. And in his spare time, he's helping startups figure out their software development and infrastructure needs. Previously, he did various work around the Linux Kernel.", "public_name": "Yannick Brosseau", "guid": "20bc65cc-454a-5071-96ee-86bdb5452da8", "url": "https://pretalx.com/sotm2026-osm-science/speaker/98TMMP/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/EL3XUW/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/EL3XUW/", "attachments": []}, {"guid": "f074ed02-9f28-5e94-9dbb-01bcd1d0ba1c", "code": "NTXXN8", "id": 96578, "logo": null, "date": "2026-08-30T12:30:00+02:00", "start": "12:30", "end": "2026-08-30T12:35:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96578-osm-contribution-analysis-in-war-time", "url": "https://pretalx.com/sotm2026-osm-science/talk/NTXXN8/", "title": "OSM contribution analysis in war time", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This study analyzes OpenStreetMap contributions in Ukraine since the February 2022 invasion, crossing OSM building edits with ACLED conflict events and frontline geometries. Results reveal a sharp spike in edits following the invasion, concentrated around Kyiv, alongside a moderate correlation between bombardment events and mapping activity. Spatial analysis shows contributions near the frontline even in occupied areas, suggesting deletions and ruin tags as proxies for physical destruction.", "description": "As a more than twenty-year-old citizen science project, OpenStreetMap (OSM) has become a widely used geospatial platform for applications such as urban planning [3] and humanitarian response [4]. Its data can reach the same quality level as authoritative data in western countries [2]. In many developing regions, it represents the main source of geographic information regarding roads or buildings, where no national mapping agency exists.\nOSM has proven particularly valuable in the context of short-term natural disasters [1] such as in Haiti or Japan. There, the roads can be updated rapidly to support rescue missions. But the past decade has brought the return of a large-scale, long-duration human catastrophy named \"war\". Unlike natural disasters, these events unfold over years and across vast territories, raising new questions about how mapping communities respond over time: how does editing activity evolve in relation to conflict dynamics, and how close to the active frontline are OSM data updated?\nThis study analyzes OSM contributions in Ukraine during the ongoing war, with the objective of understanding how mapping activity responds to long-duration armed conflict. Two main axes are explored: (i) the spatio-temporal relationship between OSM building edits including deletions, ruin-related tags, aerial bombardment events, and (ii) the evolution of OSM contribution levels in the vicinity of the active frontline over time. The analysis focuses primarily on buildings, which represent the most informative object type for this context with nearly 7.5 million objects in Ukraine according to OSM Taginfo.\nArea of study. Our area of study is the whole country of Ukraine within its internationally recognized pre-war borders, including Crimea, as all our data providers treat it as part of the country. Ukraine was chosen over other active conflicts (Gaza, Iran) because its mapping culture aligns closely with western OSM practices, resulting in high baseline contribution levels and a richer edit history to analyze. Russia and Belarus are excluded, as we hypothesized that OSM contributions in both countries may have declined during the war.\nData and methods. The data and methods are summarized in Figure 1. The first column features the data, the second the methods and the third the theme of the research.\nOSM history data were retrieved using the ohsome API, which exposes the full edit history of OSM objects. We also used Geofabrik to extract Ukraine's administrative boundaries. Conflict data were sourced from the Armed Conflict Location & Event Data (ACLED) global monitor, providing georeferenced records of air strikes and shelling events. Frontline geometries were assembled from three complementary sources to cover the full conflict period: Institute for the Study of War (ISW) shapefiles for February 2022, ACLED battle events as a spatial proxy from March 2022 to June 2024, and DeepState daily files from July 2024 onwards. For each time step, the distance from OSM-modified building centroids to the contemporary frontline was computed. This allowed the spatial relationship between editing activity and conflict intensity to be tracked over time.\nResults. Preliminary results reveal a strong temporal signal following the February 2022 invasion. As shown in Figure 2, a sharp spike in building-related edits occurred in the weeks immediately after the invasion, concentrated primarily around Kyiv, before activity progressively redistributed across the country. Year-on-year comparisons between February and March 2022 and the same period in 2023 confirm that this initial surge was not sustained. This suggests that the humanitarian mapping response was most intense in the earliest phase of the conflict.\nSpatial analysis shows that OSM contributions do not concentrate exclusively in safer areas: significant editing activity is observed near the frontline, even as it shifted over time. Areas experiencing intense bombardment display heterogeneous behaviors, with both increases \u2014 consistent with damage mapping by remote volunteers \u2014 and local decreases, likely reflecting contributor displacement or infrastructure disruption. Figure 3 shows the perfect example of an area inside the frontline (in green) with multiple clusters of contributions. A moderate positive Pearson correlation between monthly conflict event counts and OSM modification counts per spatial unit in the first year of the conflict suggests that bombardment events act as a partial trigger for mapping activity. This also highlights the significant role of remote contributors whose activity is decoupled from local ground conditions.\nThese findings provide new insights into the behavior of volunteered geographic information (VGI) communities in long-duration conflict settings and open avenues for further research on the role of VGI in crisis-affected territories.\nRoads, with nearly 3 million objects according to OSM Taginfo, are considered as a perspective for future work. Indeed, roads also have strategic importance in war, so updating them may give an advantage to the enemy. Another perspective of this project is the analysis of contributors to see if these edits are done on the field by locals or by image analysis. All scripts and processed datasets used in this study are openly available at a repository to ensure full reproducibility of our results: https://anonymous.4open.science/r/osm-war-ukraine-2737. Raw conflict data from ACLED are not included in the repository as they require a dedicated access request through the ACLED researcher tier, available on their website. Although, it is simple to obtain access to that data thanks to the ACLED team.", "recording_license": "", "do_not_record": false, "persons": [{"code": "BRDHMA", "name": "Amine Chebil", "avatar": null, "biography": "Amine Chebil; Master 1 G\u00e9omatique, G\u00e9odata Paris, Universit\u00e9 Gustave Eiffel;", "public_name": "Amine Chebil", "guid": "2ca5cbad-bb9e-5666-ac45-3612f67c090b", "url": "https://pretalx.com/sotm2026-osm-science/speaker/BRDHMA/"}, {"code": "GXCS8M", "name": "Rapha\u00ebl Bres", "avatar": null, "biography": "Postdoc researcher at PACTE lab at Universit\u00e9 Grenoble Alpes, I am currently working on human migrations data modeling.\nI also keep some activity around my PhD subject which was around cycling mobility with VGI and authoritative data.", "public_name": "Rapha\u00ebl Bres", "guid": "2395acb4-6b01-5562-a366-cb6a1bda42f4", "url": "https://pretalx.com/sotm2026-osm-science/speaker/GXCS8M/"}, {"code": "CJJ88H", "name": "Malek Rihani", "avatar": null, "biography": "Geomatics student at GeoData Paris (ENSG) with a background in data science and GIS development", "public_name": "Malek Rihani", "guid": "af31ee60-8d68-565d-b438-6a3731747b64", "url": "https://pretalx.com/sotm2026-osm-science/speaker/CJJ88H/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/NTXXN8/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/NTXXN8/", "attachments": []}, {"guid": "179a961c-edf1-5b76-91ae-a4a49f2c6d42", "code": "YNRWXR", "id": 96808, "logo": null, "date": "2026-08-30T12:35:00+02:00", "start": "12:35", "end": "2026-08-30T12:40:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-96808-revealing-past-railway-networks-from-osm-data", "url": "https://pretalx.com/sotm2026-osm-science/talk/YNRWXR/", "title": "Revealing past railway networks from OSM data", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Many \"way\" elements in OSM are tagged \"railway.\" OpenRailwayMap builds upon them, adding numerous details. However, the lack of \"relation\", between \"ways\" no longer used as railways, hinders the analysis of abandoned lines. Knowledge from the realm of Volunteer Geographic Information can bring the missing link, while leveraging the accuracy of OSM data. This article aims to add nodes of thousands forgotten stations, and to fill hundreds gaps between scattered OSM elements, which are unaware of their former railway line affiliation. The experiment covers almost entire old railway networks of France and Belgium.", "description": "The present work deals with collecting and representing data over two centuries of railway in Western Europe. So far: France, Belgium and parts of Germany and Luxembourg (project _WERMA_). The goal is to gather all stations and stops along any railway that has been built, including rural tramways, industrial, mining and military lines. Wikipedia and OpenStreetMap are two reliable and perennial sources. A lot of \u201cvolunteer geographic information\u201d (VGI) is also available, but unfortunately also disappearing from the Internet. Helping to save a part of this endangered \u201cpatrimonial\u201d information by transferring it onto Wikidata, Wikipedia or OSM, is of great importance. This paper examines some solutions with respect to how OSM can inform about past railway lines and stops, and how WERMA data can be used to improve OSM data with the external knowledge of VGI.\n\n**Using OSM information about railways and train stations.**\nThe OpenRailwayMap(ORM) contribution provides a comprehensive mapping of the railway features of OSM. Since the launch of WERMA project (2022), we use ORM as a second background map, just on top of OSM, and in many places it has been invaluable visual help to fulfill our basic goal: to localize and geo-code stations that are not recorded in official sources (Railway companies), nor Wikipedia.For instance, today, about 3000 stations are active in France, 3500 are recorded as OSM-node tagged [railway=stop], and up to 5800 mentioned on Wikipedia \u201c_Liste de gares en France_\u201d, including many disused ones. When counting simple stops on secondary lines, urban or rural tramways, the total amounts to 17000, spreading across more than 12000 \u201ccommunes\u201d in France. Landmark places such as tunnels and bridges have been collected too. Building upon this geo-coding work, the new project purpose is to confront the locations of the WERMA features (files by country)with the OSM elements, and a few hundred misalignments have been visually detected and corrected in our datasets : the discrepancy is rarely above 200 meters, but a few dozens \u201ctrue\u201d errors have been detected during that process. Also, a few way-elements, tagged as \u2018railway\u2019, are not corresponding to any line in WERMA and should be added(e.g. \u201cindustrial lines\u201d to mines, quarries).\n\n**Improving precision and completeness.**\nAn automated process is being developed for checking the whole dataset on France and Belgium, the two countries where our work is 95% completed (as far as the 100% is checkable).  The process relies primarily on geographic closeness of OSM nodes tagged railway=stop | station, with features of the WERMA geojson file of stations. OSM data will improve the WERMA dataset in precision (position) and in number (detailed platforms in Central stations, or new lines). The process cannot be purely geometric, because mismatches augment with the local density of OSM-elements and WERMA-stops. Also, not all OSM-ways have names, or names related to railways. See Figure 1, near Angoul\u00eame, France, which displays OSM, ORM and WERMA data altogether.\nIn order to avoid frequent direct queries to OSM, we have archived an excerpt way(bbox)[\"railway\"] to work locally, during the experiment for this paper. Results could be updated through time.\n\n**Connecting dots with lines, connecting lines into a network**.\nWe have noticed that hundreds of stops are belonging to lines that have no counterpart on OSM data. In some cases, way-elements could fit, intertwined with gaps, still missing in order to restore continuity along disused, abandoned or totally erased old railway lines.\nWERMA data are structured such that any stop is related to one or several lines, and all lines are forming a graph. That graph enables to compute \u201cpaths\u201d between destinations in the global railway network (excepting a few isolated lines in mountains or islands).\n**_Description of Figure 1: station Angoul\u00eame-Grelet (just west of Angoul\u00eame, France)._**\nThis is the screen copy of WERMA display, after a \u201cclick\u201d on the map, providing information that lists all the properties of all features collected by this event:\n-\tLongitude, Latitude: 0.13108,45.64072\n-\t[WERMA] Angoul\u00eame-Grelet station on line 16EC1 at kilometer:5 and line 579306 at km:64\n-\t[WERMA] Line _16EC1 = _Chemins de Fer Economiques des Charentes #1_, alias : \u00ab _Le_petit_Mairat_ \u00bb from Angoul\u00eame to Barbezieux (48 km), closed:1939.\n-\t[WERMA] Line 579306 = _Shunt Angoul\u00e8me-Etat to Line 579000_, km:63.7 to km:69, closed: 1950\n-\t[OSM] way #996695821: abandoned\n-\t[OSM] way#996695821: razed\n-\tLocation on territory of Angoul\u00eame, code INSEE:16015 : from public data IGN \uf0d2\nThe popup box (Fig. 1) prints only a part of the availableVGI detailed above. The two OSM-way elements have no \u2018name\u2019 and their association to either 16EC1 or 579306, not possible directly, needs another query to disambiguate them. It can be coded if another stop exists on one of these lines.\nDo it yourself: https://bigbugdata.com/werma/v3.5/?france&center=0.13108,45.64072&zoom=17\n(be patient: loading may take\uf0bb60 seconds)\n\n**Improving both OSM and WERMA data through data reconciliation.**\nThe external knowledge is a compilation of VGI sources, which allows to build a railway graph by linking line pieces as railway lines, and to attach stops in correctorder along these lines. Just as it is the case for railway lines recorded as relations in OSM. These relations exist for exploited, or recently disused rail lines. The ambition is to extend these relations to older lines. \nThe illustration (Fig. 2) shows an example, around the city of Chateaubriand (West of France), of what can be done by using VGI knowledge and adding new relational knowledge to pairs or lists of way elements.\n**_Description of Figure 2: Combining OSM and VGI data for reciprocal improvement_**\nBackground data are OSM elements displayed by the OpenRailwayMap (lines, names of stations and ways). Additional data \u2014 the red dots \u2014are VGI geocoded stations from WERMA dataset, and red dotted vertices connecting red dots of a same line in correct order, which approximates that rail line, also allowing to compute paths, for instance.\nRectangular numbers and associated arrows are added to point out a few situations:\n-\tRectangle 1: shows gaps in OSM data on the railway heading South-West from Chateaubriand\n-\tRectangle 2: shows that some way names identify them as railway lines (466000 going North, and 519000 going South), or relate to \u201cdepartmental roads\u201d (D163 and D40, both going South-East)\n-\tRectangle 3: indicates a railway line known by VGI, unknown in OSM\n-\tRectangle 4: the geocoding of some VGI dots can be improved.\nWhile most VGI red dots are close to OSM ways, at the scale of Fig. 2, positioning can be improved by \u201csnapping\u201d their coordinates to the closest coordinates of the geometry of the way to which they will be associated. A win-win combination that improves both data sources.\n\nIn order to provide a more global approach to this work, the combination and preservation of VGI is being done via Wikidata, where all identifiers can be associated: namely OSM, Wikipedia and other VGI sources either brought to Wikidata, or extracted directly from other VGI sources (history club, ...).", "recording_license": "", "do_not_record": false, "persons": [{"code": "NYSWYT", "name": "Robert Jeansoulin", "avatar": "https://pretalx.com/media/avatars/LK7ZHH_XewyPj8.webp", "biography": "PhD computer science (Univ Toulouse), Doctor Ingeneer (ENSEEIHT INP-Toulouse), Professor (Univ. Orsay, Paris Saclay, Univ. Laval Quebec), Directeur de Recherche CNRS, Emeritus.", "public_name": "Robert Jeansoulin", "guid": "08ca8f65-9e12-571a-8d0b-b5f6107f9aec", "url": "https://pretalx.com/sotm2026-osm-science/speaker/NYSWYT/"}, {"code": "3RLQFG", "name": "Philippe Gambette", "avatar": "https://pretalx.com/media/avatars/BL7RDM_PoSD1BA.webp", "biography": "Philippe Gambette is a professor of Computer Science at Universit\u00e9 Gustave Eiffel, in the Gaspard-Monge Computer Science Laboratory (LIGM). His research in digital humanities focuses on developing methods and tools \u2014sometimes inspired by bioinformatics\u2014 for computer-assisted text analysis. In particular, he has worked on tools for visualization and digital mapping of texts, as well as automatic modernization of 17th-century texts in French.", "public_name": "Philippe Gambette", "guid": "b35e54ea-3b4f-5e6a-98e4-d106586119d5", "url": "https://pretalx.com/sotm2026-osm-science/speaker/3RLQFG/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/YNRWXR/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/YNRWXR/", "attachments": []}, {"guid": "e5284cb2-0a0d-577b-be7c-f15ab2911f41", "code": "LKLQFT", "id": 98139, "logo": null, "date": "2026-08-30T12:40:00+02:00", "start": "12:40", "end": "2026-08-30T12:45:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-98139-streetmeasure-a-low-cost-open-source-framework-using-monocular-depth-estimation-supporting-osm-measurement-tags-from-360-streetview-photos-the-case-of-sp6", "url": "https://pretalx.com/sotm2026-osm-science/talk/LKLQFT/", "title": "StreetMeasure - A low cost open source framework using monocular depth estimation supporting OSM measurement tags from 360 streetview photos, the case of SP6", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "The StreetMeasure framework addresses the critical lack of road dimension (width=*, lanes=*) in OpenStreetMap. Applied to SP6 provincial road (Piacenza, Italy), by utilizing DA360 for monocular depth estimation and SAM 2 for segmentation, it reconstructs 3D point clouds from distant crowdsourced street-level imagery, which was difficult for traditional photogrammetry. Then it aligns generated depth data with high-resolution satellite transects to recover scale.  This open-source pipeline enriches OSM tags, supports provincial infrastructure management, and enables immersive urban planning simulations.", "description": "Digital Twins (DTs) in contemporary urban planning and management is urgent, as they provide virtual interaction and simulation that overshadows traditional reliance on on-site visits. This technological shift demands advanced services from the mere passive storage and visualization of conventional geoportals. OpenStreetMap (OSM) is one open global scale platform of geospatial data, storing a great number of civil infrastructures, particularly buildings and streets (Zhou et al., 2022). Recent studies show that OSM elements with highway tags are potentially used as digital twin street models (Duque et Brovelli, 2025; Scalas et al., 2022). However, they usually missed width and lane number attributes, only 1.14% element with \u201chighway tags\u201d has also included \u201cwidth\u201d tag, and 6.61% with \u201clanes\u201d tag(Taginfo. ,n.d.).\n\nOSM elements are usually mapped from satellite images, which has certain limitations for measuring detailed dimensions with low resolution. To overcome this problem, there are studies extracting street characters as materials and dimensions from crowdsourcing streetview imageries (SVI) using mostly image segmentation and photogrammetry (Kapp et al., 2025; ). A critical need is reliable remote physical measurement technique, working with even a small amount of streetview images where traditional photogrammetry usually fails. To bridge this data gap, this research proposes state-of-the-art Monocular Depth Estimation (MDE) technologies with careful evaluations.\n\nA novel, scalable framework, called StreetMeasure, is developed to support 3D reconstruction of the street environment. The system is built around a crowdsourcing model, allowing users to contribute data through cloud based or locally-hosted SVI services (e.g., Mapillary, Panoramax). Upon requesting a reconstruction, the system processes the image via certain MDE models, aligning with existing photogrammetric sparse models or low-resolution objects detected from satellite images, providing visualization of the results and showing them on a web map, alongside comparative metrics on model inference time and precision. A crucial part of the framework is the integration of a validation loop, where users can perform and input on-site physical measurements to iteratively validate and refine the inferred depth data. These results could be reused to enhance future predictions. Furthermore, to guarantee operational flexibility and responsiveness across diverse field conditions, the framework is designed to operate efficiently on edge devices.\n\nAs part of a strategic transition towards open-source ecosystems, the Province of Piacenza (Italy) implemented a digital road cadaster using PostGIS, LizMap, QGIS and QField technologies. to optimize the management of provincial infrastructure (Gaspari et al., 2023). Following the release of Italian guidelines for extraordinary transport, there is an increasing need for innovative, low-cost approaches to updating relevant characteristics (such as width, surfaces, curves angles) of road graphs to develop better strategies in identifying optimal routes.\nTaking the 16 km-long SP6 between the municipalities of Piacenza and Carpaneto Piacentino as the main case study, this project aims to integrate traditional mapping approaches by integrating open, crowdsourced data and AI, while measuring the width of each lane in different road cross-sections (see Figure 1). Although street-level imagery from Mapillary and 20-cm resolution orthophotos from _Regione Emilia Romagna_ are openly available, the 360\u00b0 sequences captured  from car-mounted cameras at 13-m intervals lack the required density of mutual points for traditional photogrammetric reconstruction. To overcome these technical limitations, this research explores AI-driven alternatives to extract measurements from existing open data, enriching OSM data as the basis for the updated graph of the Province. All digital outputs will be validated through in-situ measurements to ensure regulatory compliance.\nInitially, a set of 714 panoramic images have been downloaded and ordered sequentially (see Figure 2). Then, using Segment Anything Model 2 (SAM 2), we masked the \u201croad\u201d element in both streetview and satellite images. Then streetview images are passed through Depth Anything in 360\u00b0(DA360, cite) to reconstruct a 3D scaled point cloud model  (see Figure 3). A number of transects are set along the street in both satellite and street view images in order to recover the scale. To evaluate the quality of this framework, the study compared the estimated width of the lane with ground truth measurement. Later, from the sequence of point clouds, it is possible to update width (for total width), lanes( for number of lanes) tags on OSM.\n\nThis framework establishes a highly scalable not only in mapping other areas, but also measuring different civil elements, e.g. bridges or tunnels. Moreover, the blended data of OSM and point clouds can serve as a powerful simulating environment capable of hosting virtual field trips and immersive planning exercises. Ultimately, StreetMeasure can support urban planning workshop tools, uniquely enabling comparative analysis and rapid assessment of project impact by simulating clear \u2018before-and-after\u2019 scenarios in a collaborative virtual space.\n\nThis study proposes a framework using up-to-date open source technology extracting information from SVI and SI in order to fulfill OSM data. It has been applied in a practical case of SP6, with actual problems of both quantity and quality of crowdsourcing data. Study found that in the field of DT, OSM can be placed in the centre of a framework. At one point OSM stores data from various public surveys, while from another point OSM becomes a primary dataset for DT applications.", "recording_license": "", "do_not_record": false, "persons": [{"code": "97AVRG", "name": "Quang Huy NGUYEN", "avatar": "https://pretalx.com/media/avatars/QYKQRM_i2Yoc9k.webp", "biography": "Quang Huy was born in Hanoi, Vietnam, in 1996. He obtained B.Sc. in Urban and Regional Planning (Hanoi Architectural University, 2019), MSc in Urban project, Heritage and Sustainable Development (ENSA Toulouse and Hanoi Architectural University, 2022) and MSc in Landscape Architecture, Land Landscape Heritage in (Politecnico di Milano, 2024). Currently, he is currently a PhD student at the GEOlab of Politecnico di Milano. He is interested in data analyst and visualization, open-source technology, cultural heritage interpretation, museum technology and scenography, GIS and geographic information technology.", "public_name": "Quang Huy NGUYEN", "guid": "33c915c0-2f85-58ff-a552-762ceb2e26fb", "url": "https://pretalx.com/sotm2026-osm-science/speaker/97AVRG/"}, {"code": "LLW3SM", "name": "Federica Gaspari", "avatar": "https://pretalx.com/media/avatars/Z3KKPS_cOOTqzr.webp", "biography": "PhD student in Geomatics and Geoinformatica at Politecnico di Milano (Italy). Co-founder of PoliMappers and active mapper member of the OSM community since 2016.", "public_name": "Federica Gaspari", "guid": "3ec4f9fc-3932-5ff7-ac8f-58f32be52cf9", "url": "https://pretalx.com/sotm2026-osm-science/speaker/LLW3SM/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/LKLQFT/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/LKLQFT/", "attachments": [{"title": "Presentation", "url": "/media/sotm2026-osm-science/submissions/LKLQFT/resources/2026H_LQjVQkx.pdf", "type": "related"}]}, {"guid": "9740f2f9-869b-5a68-a9fd-c54cd479c3c0", "code": "UNTDRT", "id": 98144, "logo": null, "date": "2026-08-30T12:45:00+02:00", "start": "12:45", "end": "2026-08-30T12:50:00+02:00", "duration": "00:05", "room": "Martinique", "slug": "sotm2026-osm-science-98144-terraink-lowering-barriers-to-openstreetmap-data-consumption-a-usage-study-of-an-open-source-web-application", "url": "https://pretalx.com/sotm2026-osm-science/talk/UNTDRT/", "title": "Terraink: Lowering Barriers to OpenStreetMap Data Consumption - A Usage Study of an Open-Source Web Application", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Terraink is an open-source web application that turns OpenStreetMap data into printable city map posters without requiring installation, programming, or GIS expertise. I analyze its first 60 days as a mixed-methods natural experiment to examine whether lowering technical barriers reveals latent demand for OSM-derived outputs beyond expert user communities.", "description": "Research on OpenStreetMap within the broader literature on volunteered geographic information has primarily focused on data quality, contribution patterns, and community processes [1-6]. By contrast, much less attention has been paid to people who consume OSM-derived outputs without participating in mapping and without possessing the technical skills normally required to access raw or semi-processed OSM data. In practice, many pathways into OSM consumption remain mediated by expert tools such as QGIS, Overpass, Python libraries, or command-line workflows [3,4]. These are powerful, but they also define an access boundary: they favor technically confident users and exclude many people who may nevertheless find value in OSM-derived artifacts. This paper investigates that boundary empirically.\n\nThe paper centers on Terraink, an AGPL-licensed web application that lets users generate printable map posters from OSM data directly in the browser. An example of the resulting OSM-derived poster output is shown in Figure 1. Terraink was directly inspired by an earlier Python-based tool, MapToPoster, that demonstrated interest in this type of output but required a programming environment and command-line use. I treat Terraink not simply as an application case study, but as a controlled translation of a pre-existing OSM use case across an accessibility boundary: the core output remains substantially the same, while the technical demands placed on the user are sharply reduced. This makes the release of Terraink analytically useful for examining whether reduced friction changes the scale, geography, and character of OSM data consumption.\n\nThe aim of the study is threefold. First, I ask whether lowering technical barriers can unlock substantial latent demand for OSM-derived outputs among non-expert users. Second, I examine what kinds of social uses emerge when OSM data are encountered through a highly accessible, aesthetically oriented interface rather than through conventional expert tooling. Third, I document the infrastructural consequences of success at that accessibility boundary, particularly when a suddenly popular downstream client depends on shared free upstream services.\n\nMethodologically, the paper adopts a mixed-methods design based on the first 60 days after Terraink's public release. The quantitative component combines Cloudflare web analytics, GitHub repository metrics, and aggregate tile-request volumes reported by OpenFreeMap, the upstream tile provider used by the application. These sources are used to characterize traffic scale, temporal growth, international reach, and the intensity of use. The qualitative component consists of exploratory thematic analysis of unsolicited user feedback collected across GitHub Issues, email, Reddit, Threads, and Instagram direct messages. Because this material was not elicited through a survey instrument, I do not treat it as representative of the full user population. Instead, I use it to identify recurring patterns in how users describe the value of the output, the contexts in which they use it, and the language through which non-expert consumers relate to OSM-derived artifacts. A third evidential stream comes from direct communication with the OpenFreeMap maintainer, who reported that Terraink had become the single largest source of tile requests on the service, peaking at 36 million requests before optimization. I use this as external corroboration that the observed traffic had effects beyond the application itself. The growth curve is shown in Figure 2.\n\nThe findings indicate that the accessibility shift matters substantially. In its first 60 days, Terraink reached approximately one million unique users and generated around 30 million requests. Distribution was organic and international, with users sharing the tool in more than twelve languages, including Chinese, Russian, Arabic, Polish, Serbian, German, English, Spanish, French, Portuguese, and Italian. Growth was strongly non-linear: the second month showed a marked inflection associated with independent multilingual creators rather than any formal marketing campaign. This pattern suggests that, once the technical threshold is reduced sufficiently, OSM-derived outputs can circulate through mainstream social channels that sit largely outside the established OSM expert ecosystem. Cloudflare request counts and visitor totals are summarized in Figure 3.\n\nThe qualitative material adds interpretive depth to this usage pattern. Preliminary thematic analysis identifies four recurring use contexts: personal commemoration, gifting and decoration, travel memory, and urban discovery. Across these categories, users frequently valued the map not as a technical dataset but as a personal representation of meaningful places. They described prints of hometowns, neighborhoods, holiday destinations, and life events; they framed the output as a gift object or domestic artifact; and they often engaged with the city map as a medium for memory, attachment, or aesthetic appreciation. A notable result is that many users did not know, and often did not need to know, that the underlying spatial data originated in OpenStreetMap. This does not reduce OSM's significance; rather, it indicates that part of OSM's social value may be realized indirectly, through downstream artifacts whose accessibility obscures the technical provenance of the data while expanding its public reach.\n\nThe infrastructural findings are equally important. Terraink's early growth produced a level of tile demand that imposed visible pressure on a shared open provider. After optimization work including request batching, zoom-aware tile loading, client-side caching, and request shaping in MapLibre, the system could sustain a peak day of 80 million tile requests. This episode shows that accessibility-oriented OSM applications are not only interface interventions; they are also infrastructural actors. If barrier-lowering tools succeed, they can scale far more quickly than expected and can unintentionally stress common resources. For the OSM research community, this suggests that accessibility should be analyzed together with infrastructural responsibility. Designing for non-expert uptake must include planning for ethical and technically sustainable use of upstream commons.\n\nThe paper makes three contributions to OSM research. First, it provides empirical evidence that there is substantial latent demand for OSM-derived consumption outside expert communities, and that this demand may be unlocked more effectively by reducing technical barriers than by increasing feature complexity. Second, it offers a systems-level account of what happens when a successful downstream accessibility tool meets shared open infrastructure, showing why operational sustainability must be part of the design and evaluation of OSM-facing applications from the outset. Third, it proposes a preliminary taxonomy of non-expert OSM consumption in which the strongest articulated forms of value are emotional, commemorative, aesthetic, and place-based. In this respect, it complements a literature that has largely examined contributor demographics, participation biases, and data quality [2,5,6].\n\nThe scientific contribution therefore lies not in presenting Terraink as a novel product, but in using its release as an observational window onto a poorly studied population and process: non-expert consumption of OSM-derived outputs under conditions of substantially reduced technical friction. More broadly, the paper argues that OSM research should treat accessibility interventions as research instruments that can reveal hidden demand, neglected user groups, and infrastructural externalities that remain invisible when analysis is confined to contributors or technical users.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XVGL38", "name": "Yousif Amanuel", "avatar": "https://pretalx.com/media/avatars/VRRMDE_sLbE1jQ.webp", "biography": "Yousif Amanuel is a PhD researcher, engineer, and maker whose work circles a single instinct: making powerful tools accessible to people who shouldn\u2019t need a command line to use them. At Leibniz University Hannover, his doctoral research focuses on computer science, AI, and machine learning education, using block-based programming to make these subjects approachable for learners. Outside academia, he is the creator of Terraink, an open source web app that turns OpenStreetMap data into printable city map posters and reached 1 million users in two months with zero marketing. He recently shipped Clawd Mochi, an open source ESP32 desk companion inspired by the Claude mascot, which picked up 200+ GitHub stars in two weeks. He served two years as president of the Erasmus Student Network at his university and shares his projects with around 27,000 followers across Instagram and TikTok. Outside of code, Yousif procrastinates productively: he started Terraink to procrastinate his PhD, then started Clawd Mochi to procrastinate Terraink. He is a relentless DIY learner who cuts his own hair, prints his own T-shirts, and rarely meets a new skill he doesn\u2019t want to try for himself. He also speaks four languages fluently.", "public_name": "Yousif Amanuel", "guid": "a04fe6f7-9e63-50e5-91bb-aa469c80bc77", "url": "https://pretalx.com/sotm2026-osm-science/speaker/XVGL38/"}], "links": [], "feedback_url": "https://pretalx.com/sotm2026-osm-science/talk/UNTDRT/feedback/", "origin_url": "https://pretalx.com/sotm2026-osm-science/talk/UNTDRT/", "attachments": [{"title": "Presentation", "url": "/media/sotm2026-osm-science/submissions/UNTDRT/resources/SotM__qzR9rFh.pdf", "type": "related"}]}]}}]}}}