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.

How we help Designing a solar power system for a home is a process that relies on factors unique to each home. First, we model the roof in three dimensions to account for obstructions such as chimneys and vents. Second, we lay legally-mandated access walkways and place solar panels on the roof segments. Finally, we model the angle and exposure of sunlight hitting the roof to calculate the system's potential energy production. With Instant Design, we replicate this same process by leveraging tools including machine learning and optimization.