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.

翻译：Project Sunroof项目利用高质量航空数据评估住宅建筑的太阳能潜力，即估算在屋顶安装太阳能电池板后建筑可捕获的潜在太阳能（以及相应的经济收益）。遗憾的是，其覆盖范围受限于高分辨率数字表面模型（DSM）数据的缺乏。我们提出一种深度学习方法，通过增强广泛可用的低分辨率数据来弥补这一不足，从而显著扩大Sunroof的覆盖范围。同时，我们介绍了通过用深度学习替换Sunroof处理流程中的某些算法组件来进一步提升精度的持续研究进展。