Estimating Residential Solar Potential Using Aerial Data
Ross Goroshin Alex Wilson Andrew Lamb Betty Peng Brandon Ewonus Cornelius Ratsch Jordan Raisher Marisa Leung Max Burq Thomas Colthurst William Rucklidge Carl Elkin
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.