Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping
This work addresses the need for global solar potential assessment to promote renewable energy adoption, representing an incremental improvement by extending existing methods to new data sources.
The paper tackled the problem of expanding Google's Solar API to global coverage by using satellite imagery to create high-resolution Digital Surface Models (DSMs) and roof segmentations, achieving a DSM MAE of ~1m on buildings, ~5-degree roof pitch error, and ~56% IOU on roof segmentation.
The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption.