Solar Panel Segmentation on Aerial Images using Color and Elevation Information

The automatic detection of solar panels from aerial imagery is highly desirable for energy planning and urban development in the Netherlands, where such data has not been extensively explored.To address this gap, we publicise a new annotated dataset, tailored for the Dutch landscape, and compare several state-of-the-art semantic segmentation models.While traditional approaches primarily utilize RGB data, we incorporate elevation and angle information in the model to analyse its potential benefit.We achieved satisfactory performance for automated solar panel detection and segmentation, with surface estimates only diverging by 1m within a 900m 2 area.The additional elevation information does not improve the performance significantly, but is more robust in certain cases.