Recent advances in remote sensing have shifted the focus from the analysis of individual image scenes to the understanding of complex earth systems. That means the analysis of dynamic evolutions replaces previous static examinations for fixed time points. Furthermore, interdisciplinary research and the integration of heterogeneous data sources are characterizing this transformation process. Digital Elevation Models (DEMs) are predestined for supporting this process by supplementing orthophotos and map data. Promising applications include city planning, landslide analysis, and flood risk assessment where spatio-temporal change detection is a central concept to be applied. Concerning map data, the OpenStreetMap (OSM) project, based on the idea of Volunteered Geographic Information, has revolutionized the effective production and update of digital maps. However, OSM data do not include elevation information and often contains incorrect geometric information in the built environment. In this paper, we introduce a self-training framework for evaluating OSM building footprints with the aid of high-resolution DEMs. The framework supports building segmentation with a weakly supervised approach to improve the representation of OSM building footprints. The availability of DEMs is used to check the quality of OSM data. The applicability of the proposed approach is demonstrated through a case study in Karlsruhe, Germany, showing promising performance in evaluating OSM building footprints. With our approach, change detection of OSM data can also be carried out using different temporal versions of DEM and OSM data. Finally, conclusions are drawn from the presented approach and an outlook is presented on future research on spatio-temporal DEMs.