Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping

The transition to renewable energy sources such as solar is crucial for mitigating climate change. Google Maps Platform Solar API aims to accelerate this transition by providing accurate estimates of solar potential for buildings covered by aerial imagery. However, its impact is limited by geographical coverage and data availability. This paper presents an approach to expand the project's capabilities using satellite imagery, enabling global-scale solar potential assessment. We address challenges specific to satellite imagery, such as lower resolution and oblique views, by developing deep learning models for Digital Surface Model (DSM) estimation and roof plane segmentation. The models are trained and evaluated on datasets comprising of spatially aligned satellite and aerial imagery. Our results demonstrate the effectiveness of our approach in accurately predicting DSMs and roof segments from satellite imagery, paving the way for a significant expansion of the Solar API and impact in promoting solar adoption.