Estimating Residential Solar Potential Using Aerial Data
This addresses a domain-specific problem for solar energy assessment by improving data availability, though it appears incremental as it builds on an existing pipeline.
The paper tackles the limited coverage of Project Sunroof in estimating residential solar potential due to lack of high-resolution data by introducing a deep learning method to enhance low-resolution data, increasing coverage dramatically.
Project Sunroof estimates the solar potential of residential buildings using high quality aerial data. That is, it estimates the potential solar energy (and associated financial savings) that can be captured by buildings if solar panels were to be installed on their roofs. Unfortunately its coverage is limited by the lack of high resolution digital surface map (DSM) data. We present a deep learning approach that bridges this gap by enhancing widely available low-resolution data, thereby dramatically increasing the coverage of Sunroof. We also present some ongoing efforts to potentially improve accuracy even further by replacing certain algorithmic components of the Sunroof processing pipeline with deep learning.