Assessing the Quality of OSM 3D Building Data Using 3D Geometric Comparison and Machine Learning

ABSTRACT OpenStreetMap (OSM) is a well‐known platform for Volunteered Geographic Information. Most studies on OSM 3D building quality focus on a single aspect, such as footprint shape errors or attribute completeness. Current machine learning approaches typically analyze 2D footprints, with little attention paid to 3D characteristics like roof geometry or building height. To fill this gap, we propose an integrated framework that combines geometric comparative evaluation with machine‐learning prediction. In this framework, based on footprint Intersection over Union (IoU) and centroid distance, OSM buildings are matched with authoritative or externally validated 3D reference datasets selected for each study area, for example, from national mapping agencies. For the matched OSM buildings, the spatial accuracy is evaluated using location error, height error, building area error, and 3D Hausdorff distance error. For the unmatched buildings, machine learning models are used to predict their quality. We validated the proposed framework in six study areas across Asia and Europe: Beijing, Ningbo, Osaka, Yokohama, Amsterdam, and Almere. In total, the evaluation covers more than 115,000 OSM buildings. Compared with traditional methods, the proposed framework significantly improves facade error detection, evaluation completeness, and the granularity of quality classification, offering new insights for assessing and improving OSM data quality. The results produced in this study are available at: https://github.com/Longtao2002/OSM‐3D‐Building‐Quality‐Assessment .