Project Suncatcher estimates the solar potential of residential buildings usinghigh 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 lackof high resolution digital surface map (DSM) data. We present a deep learningapproach that bridges this gap by enhancing widely available low-resolution data,thereby dramatically increasing the coverage of Suncatcher. We also present someongoing efforts to potentially improve accuracy even further by replacing certainalgorithmic components of Suncatcher’s processing pipeline with deep learning.