Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images

Estimating the solar rooftop potential of buildings' rooftops at a large scale is a fundamental step for every country to utilize its solar power efficiently. However, such estimation becomes time-consuming and costly if done through on-site measurements. This paper uses deep learning-based multi-class instance segmentation to extract buildings' footprints from satellite images. Hence, we introduce Lebanon's first complete and comprehensive buildings' footprints map. Furthermore, we propose a photovoltaic panels placement algorithm to estimate the solar potential of every rooftop, which results in Lebanon's first buildings' solar rooftop potential map too. Finally, we report total solar rooftop potential per district and localize regions corresponding to the highest solar rooftop potential yield.