Estimating Residential Solar Potential Using Aerial Data

• URL: http://arxiv.org/abs/2306.13564v1
• Date: Fri, 23 Jun 2023 15:37:21 GMT
• Title: Estimating Residential Solar Potential Using Aerial Data
• Authors: Ross Goroshin, Alex Wilson, Andrew Lamb, Betty Peng, Brandon Ewonus, Cornelius Ratsch, Jordan Raisher, Marisa Leung, Max Burq, Thomas Colthurst, William Rucklidge, Carl Elkin
• Abstract summary: Project estimates the solar potential of residential buildings using high quality aerial data. Project's 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.
• Score: 0.4811810722979911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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