State of the Map 2021 - Academic Track

Damien Graux

Damien Graux (https://dgraux.github.io/) is a tenured researcher at Inria Sophia Antipolis based in the Wimmics group. He has been contributing to research efforts in Semantic Web technologies and focusing on distributed query evaluation and on designing complex transformation pipelines for heterogeneous Big Data. Prior to joining Inria, he had research positions at Trinity College Dublin (Ireland) and at Fraunhofer IAIS (Germany).

  • Involvement of OpenStreetMap in European H2020 Projects
Dipto Sarkar

GIScientist

  • Community Interactions in OSM editing
Elias Nasr Naim Elias

Elias Nasr Naim Elias is a PhD Student in Geodetic Science (Concentration Area: Cartography and GIS) at the Federal University of Parana, Brazil. He researches about Geospatial Data Quality, Collaborative Mapping and new technologies for the generation and update of geospatial data.

  • A proposal for a QGIS Plugin for Spatio-temporal analysis of OSM data quality: the case study for the city of Salvador, Brazil
Jennings Anderson

Jennings Anderson is a GeoInformation Scientist specializing in OpenStreetMap. He received his PhD in computer science from the University of Colorado Boulder where he developed contributor-centric research approaches to OpenStreetMap data analysis. He collaborates closely with both academic and industry researchers to understand the evolution of OpenStreetMap as a database and a community of mappers.

  • Community Interactions in OSM editing
  • Introducing OpenStreetMap User Embeddings: Promising Steps Toward Automated Vandalism and Community Detection
Levente Juhász

Levente is faculty at Florida International University in Miami, FL, where he oversees applied geospatial technology projects, teaches GIS and conducts research. He is originally trained as a geographer, and earned his PhD in Geomatics from the University of Florida. His research interests revolve around increasing our understanding of Volunteered Geographic Information and user-generated geodata, including the behavior of underlying communities. He is an avid open source and open data advocate.

  • Towards understanding the temporal accuracy of OpenStreetMap: A quantitative experiment
Maxwell Owusu

Maxwell Owusu is a geospatial analyst and researcher in Geoinformatics, Land Use Change, and he has been developing free and open source framework for large scale urban applications. He has conducted research in the use of OpenstreetMap and earth observation for mapping urban poor communities as well as analyzing local knowledge.

  • Towards a framework for measuring local data contribution in OpenStreetMap
Peter Mooney

Peter Mooney is an assistant professor of Computer Science at Maynooth University, Ireland. He's been involved in research related to OpenStreetMap since around 2007. Peter has been involved in the SotM academic track since its inception.

  • What has machine learning ever done for us?
Simon Will

I am a casual OSM mapper from Heidelberg, Germany and I am almost finished with my Master’s degree in computational linguistics. I’m especially interested in building an interface for querying OSM in natural language.

  • NLMaps Web: A Natural Language Interface to OpenStreetMap
Thibaud Michel

Thibaud Michel (http://tyrex.inria.fr/people/thibaud.michel/) is a senior researcher at Wemap based in Montpellier, France. He has spent his last 8 years working on improving augmented reality applications based on geolocation, including sensors fusion for indoor location and navigation. Previously, before joining Wemap, he obtained a doctorate from the University of Grenoble Alpes and worked for several years at Inria Grenoble.

  • Involvement of OpenStreetMap in European H2020 Projects
Veniamin Veselovsky

Graduate of mathematics and international at the University of Toronto, going on to studying Digital Humanities at EPFL.

  • An Automated Approach to Identifying Corporate Editing Activity in OpenStreetMap
Yinxiao Li

Yinxiao Li is a Software Engineer at Facebook.

  • Introducing OpenStreetMap User Embeddings: Promising Steps Toward Automated Vandalism and Community Detection