Estimating Residential Solar Potential Using Aerial Data

ICLR 2023 - Tackling Climate Change with Machine Learning Workshop 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 pan...

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Main Authors Goroshin, Ross, Wilson, Alex, Lamb, Andrew, Peng, Betty, Ewonus, Brandon, Ratsch, Cornelius, Raisher, Jordan, Leung, Marisa, Burq, Max, Colthurst, Thomas, Rucklidge, William, Elkin, Carl
Format Journal Article
LanguageEnglish
Published 23.06.2023
Subjects
Computer Science - Computer Vision and Pattern Recognition
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Summary:ICLR 2023 - Tackling Climate Change with Machine Learning Workshop 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.
DOI:10.48550/arxiv.2306.13564