Proceedings of the Academic Track, State of the Map 2022 August 19 - 21, 2022 | Florence, Italy Inequalities in the completeness of OpenStreetMap buildings in urban centers Benjamin Herfort 1,2 *, Sven Lautenbach 1 , João Porto de Albuquerque 3 , Jennings Anderson 4 and Alexander Zipf 2 1 Heidelberg Institute for Geoinformation Technology, Heidelberg, Germany; benjamin.herfort@heigit.org, sven.lautenbach@heigit.org 2 GIScience Chair, Institute of Geography, Heidelberg University, Heidelberg, Germany; zipf@uni-heidelberg.de 3 Urban Big Data Centre, University of Glasgow, United Kingdom; Joao.Porto@glasgow.ac.uk 3 Meta Platforms Inc., United States; jennings.anderson@gmail.com * Author to whom correspondence should be addressed. This abstract was accepted to the Academic Track of the State of the Map 2022 Conference after peer-review. The collaborative maps of OpenStreetMap (OSM) have become a major source of geospatial baseline data for humanitarian organisations, companies and public authorities. Describing the elements of spatial data quality (e.g. positional accuracy, completeness, temporal quality) for the OSM dataset is a key prerequisite to provide the potential stakeholders with the necessary information to decide on the fitness for use of a data set for their particular application [1]. Better spatial data quality assessment would promote the adoption and (right) usage of new sources of data such as OSM and data products based on OSM. A large community of researchers has analyzed the quality of OSM data in comparison to authoritative reference data sets, by means of remote sensing and using intrinsic measures [2–4]. It has been acknowledged that the OSM data in general is strongly biased, in part due to a much larger contributor basis in countries in the Global North as a consequence of socio-economic inequalities and the digital divide [5,6]. Albeit the manifold usage of OSM building footprints, an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness in regions home to a population of 3.5 billion people (about 50% of the global population). First, we propose a machine learning regression method based on Random Forests (RF) to assess OSM building completeness within all 13,189 urban centers (as defined by the European Commission [7]). The analysis utilizes an extensive collection of open building data from commercial and authoritative sources as training data and builds upon very recent technological advances to utilize OSM full-history data for spatio-temporal data analysis on the global scale [8]. The model further relies on information obtained from remote sensing data (land cover, population distribution, night time lights), subnational human development, and urban road network density as predictors. This allows us – for the first time – to present a comprehensive assessment of the evolution of urban OSM building completeness which encompasses all data contributed to OSM since 2008. Herfort, B., Lautenbach, S., Porto de Albuquerque, J., Anderson, J., & Zipf, A. (2022). Inequalities in the completeness of OpenStreetMap buildings in urban centers. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22 DOI: 10.5281/zenodo.7004534 © 2022 by the authors. Available under the terms of the Creative Commons Attribution (CC BY 4.0) license. 38