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OpenPVMapper is a comprehensive, nationwide database of rooftop photovoltaic systems in France, created by integrating multiple data sources, including a deep learning detection pipeline from aerial imagery and building-level datasets. This effort addresses the lack of a detailed, installation-level dataset, providing a total of 1,135,850 documented installations with an installed capacity of approximately 15.01 GWp. The database achieves a precision of 74-75% through manual validation and cross-source corroboration, significantly enhancing confidence in the data's accuracy compared to existing single-source methods.
By combining diverse data sources, OpenPVMapper reveals over 1.1 million rooftop PV installations in France, setting a new standard for accuracy in renewable energy datasets.
Rooftop photovoltaic (PV) systems account for the vast majority of PV grid connections, yet no open, comprehensive, installation-level dataset of these systems exists: public registries aggregate data only above a capacity threshold, and remote sensing-based detection efforts, while extensive, are typically confined to a single method, a limited geographic scope, or a single point in time. We introduce OpenPVMapper, a nationwide, multi-source database of rooftop PV installations in mainland France, built by aggregating and reconciling complementary sources: a deep learning-based detection pipeline deployed on nationwide aerial imagery, OpenStreetMap and a probabilistic building-level detection dataset. The resulting database contains 1,135,850 installations, totaling approximately 15.01~GWp of installed capacity, each documented with its provenance, detection method, and, where available, a manual validation flag. Manual review of a stratified sample of 1,862 installations places the database's overall precision at approximately 74--75\%, with corroboration across independent sources bringing a substantial, quantified precision gain. By aggregating independent sources rather than relying on any single detection method, OpenPVMapper reaches a level of confidence beyond what any one source could provide on its own, while remaining extensible to further sources as they become available. It is released under an open (CC-BY) license alongside the full source code used to build it.