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

TL;DR

This paper presents a deep learning approach to generate high-resolution digital surface models and roof segmentation from satellite imagery, expanding solar potential assessment globally and improving upon aerial imagery limitations.

Contribution

It introduces a novel method for building detailed DSMs and roof segmentation from satellite images, enabling global solar mapping beyond aerial imagery constraints.

Findings

25cm DSM accuracy achieved

~56% roof segmentation IOU

~1m DSM MAE on buildings

Abstract

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.