Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data (Papers Track)

Daniel de Barros Soares (nam.R); François ANDRIEUX (nam.R); Bastien HELL (nam.R); Julien LENHARDT (nam.R; ENSTA); JORDI BADOSA (Ecole Polytechnique); Sylvain GAVOILLE (nam.R); Stéphane GAIFFAS (nam.R; LPSM (Université de Paris)); Emmanuel BACRY (nam.R; CEREMADE (Université Paris Dauphine, PSL))

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Buildings Power & Energy Computer Vision & Remote Sensing


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.

Recorded Talk (direct link)