The world is not just made of streets, buildings, and zones; it is shaped by how people engage and interact with places in their everyday lives. This abstract presents a web-based geospatial tool that supports the mapping of these lived places and locales named PlaceCrafter. PlaceCrafter supports researchers in identifying platial regions: functional, human-centred areas that cross administrative and formal boundaries. The framework is built on OpenStreetMap, combining (near) real-time clustering, analysis, and statistical validation of these platial regions. PlaceCrafter supports researchers in exploring the subjective experiences of place through existing datasets and city structures.

This study presents a geospatial framework that combines NLP, machine learning, and GIScience to extract and georeference tweets related to the November 2016 Haifa wildfire, enabling near real-time insights into urban fire dynamics. Using OpenStreetMap and GeoNames to geocode over 16,000 tweets, the researchers demonstrated strong spatial and temporal alignment with official fire incident reports, highlighting social media’s potential as a supplementary data source for disaster response. The approach offers a scalable model for leveraging crowdsourced and user-generated data in emergency informatics, especially in data-scarce regions.

Mangrove ecosystems are frontline defenders against climate change, yet data gaps hinder timely conservation. This research introduces a open mapping framework to detect and classify mangrove regeneration and degradation zones in the Sundarbans—one of the world’s largest mangrove forests. We fuse NDVI-based satellite analytics with OpenStreetMap (OSM) contributions and local validation to improve spatial accuracy. Our workflow strengthens OSM’s environmental data model and demonstrates how open-source mapping can guide climate adaptation, habitat restoration, and disaster risk planning. The study serves as a template for scaling environmental monitoring across other fragile coastal ecosystems.

OSMlanduse is the first EU-wide 10 m land-use map integrating 3.2 million OpenStreetMap geometries with Sentinel-2 imagery through an open deep-learning workflow. Delivering CORINE-level thematic detail with finer spatial resolution, it achieves 89% accuracy, providinng wall-to-wall coverage while retaining OSM’s sub-metre detail where available. Released under open licences with reproducible scripts, it supports applications from climate modelling and biodiversity surveys to urban planning and policy monitoring. By uniting crowdsourced mapping and Earth observation, OSMlanduse demonstrates a scalable, transparent approach to producing reliable, high-resolution land-use information at continental scale.

This talk explores the extrinsic quality of OpenStreetMap data in Brno by comparing the city’s most frequently mapped amenities against a custom, field-collected reference dataset. The findings highlight relatively high attribute accuracy in OSM but reveal gaps in feature completeness, with only about 34.94% of features matched with the reference dataset.

Humanitarian OpenStreetMap Team proposed the fAIr project (https://fair.hotosm.org/) - an open-source AI-assisted mapping tool. This study describes our user testing organized to compare AI-assisted mapping of buildings in the fAIr tool and classic manual mapping of buildings in the JOSM editor without AI assistance. 26 participants took part in the experiment. Efficiency (number of buildings mapped per minute), effectiveness (proportion of buildings mapped correctly), and satisfaction (feedback from participants) were analyzed.

This talk examines the dynamics of collective intelligence in humanitarian mapping projects coordinated through the HOT Tasking Manager, using a dataset of 746 projects and 312,289 tasks to evaluate participation, collaboration, and evidence of intelligent group behavior.

EUthMappers is an ERASMUS+ initiative that promotes STEAM education in secondary schools throughout the European Union, enhancing students' digital skills and fostering environmental civic engagement. The project includes three universities, students and teachers from five European schools, working for two years. The project has three main phases: development of training materials, local mapping projects and humanitarian mapping collaboration. This presentation outlines the project's implementation steps and showcases the remarkable results achieved by not only participating students but also organizers.

This study introduces a behavior-dependent, unsupervised machine learning approach to assess the intrinsic quality of OpenStreetMap (OSM) data in Dhaka, which is both data-starved and urbanizing rapidly urbanizing area. Leveraging enriched contributor metadata and Principal Component Analysis (PCA), latent behavioral patterns and segmented contributors identified using KMeans and HDBSCAN. The silhouette score for PCA-based clustering was 0.951. The results show superior interpretability of KMeans over HDBSCAN. This repeatable methodology provides a scalable and reference-free solution to take quality assurance of VGI datasets to the front-line, in cases of limited or no authoritative data.

Dhaka, one of the most densely populated cities in the world, faces a high risk of catastrophic damage in the event of a major earthquake. The lack of accessible open spaces poses a serious challenge for emergency evacuation and survival. This project utilizes OpenStreetMap (OSM) road and building layers, combined with satellite imagery, to identify existing open spaces across the city. Using ArcGIS, a routing model was developed to guide individuals to their nearest safe zone during an earthquake. This approach demonstrates how open-source geospatial data and GIS tools can be leveraged for disaster preparedness in high-risk areas.

In this work we develop a reproducible pipeline for querying multiple LLMs/chatbots in order to access and analyse their opinion on OpenStreetMap by prompting these systems to answer a series of questions on OSM. People are turning to chatbots and LLMs for opinions and advice on practically every topic. We believe it is important that we begin to assess how chatbots and the LLMs provide information and opinion about OSM. Among other outputs, this work can providing evidence to the OSM community that can be used to shape future public engagement strategies about the project.