By A. Yair Grinberger, Jennings Anderson, Peter Mooney, Christina Ludwig and Marco Minghini
The “Proceedings of the Academic Track at State of the Map 2021” contains 9 papers which highlight three major themes in OpenStreetMap (OSM) research: data quality, the identity of contributors and the nature of their contributions, and the use of new approaches and techniques such as machine learning within OSM data use and production. These themes, especially the latter two, are also the subject of intense and sometimes heated discussions within the OSM mapping community where questions and doubts regarding the utility and effects of corporate editors and machine-produced data are raised. This stresses the importance of the interactions between the mapping and the research community, where the latter takes inspiration from the former while the former potentially enjoys the insights provided by the latter.
- Community interactions in OSM editing
- What has machine learning ever done for us?
- NLMaps Web: A natural language interface to OpenStreetMap
- Towards a framework for measuring local data contribution in OpenStreetMap
- Towards understanding the temporal accuracy of OpenStreetMap: A quantitative experiment
- Introducing OpenStreetMap user embeddings: Promising steps toward automated vandalism and community detection
- A proposal for a QGIS Plugin for spatio-temporal analysis of OSM data quality: the case study for the city of Salvador, Brazil
- An automated approach to identifying corporate editing activity in OpenStreetMap
- Involvement of OpenStreetMap in European H2020 projects
Read or download the “Proceedings of the Academic Track at State of the Map 2021”