  Estimating the amount of electricity that can be produced by rooftop
photovoltaic systems is a time-consuming process that requires on-site
measurements, a difficult task to achieve on a large scale. In this paper, we
present an approach to estimate the solar potential of rooftops based on their
location and architectural characteristics, as well as the amount of solar
radiation they receive annually. Our technique uses computer vision to achieve
semantic segmentation of roof sections and roof objects on the one hand, and a
machine learning model based on structured building features to predict roof
pitch on the other hand. We then compute the azimuth and maximum number of
solar panels that can be installed on a rooftop with geometric approaches.
Finally, we compute precise shading masks and combine them with solar
irradiation data that enables us to estimate the yearly solar potential of a
rooftop.
