PASSION: a workflow for the estimation of rooftop photovoltaic potential from satellite imagery.

Photovoltaic (PV) and wind are currently the highest growing renewable energies according to the annual World Energy Outlook 2022. However, in the case of solar PV in Europe, this growth is mainly driven by utility-scale installations. Distributed residential generation has many benefits, such as relieving the electrical grid and an increase of self-sufficiency. One challenge of this topic is to accurately estimate the rooftop PV potential of different regions, in order to best allocate economic resources and regulate accordingly. Multiple approaches have been proposed in the past, such as infering from proxy variables like population density, automatically analyzing residential 3D point clouds or automatically analyzing satellite images. The latter has gained popularity in the recent years given the increased availability of satellite imagery and the improvement of Computer Vision methods. However, in research, the analysis of satellite imagery is impeded by the lack of transparency, reproducibility, and standardization of methods. Studies are heterogeneous, target different types of potential with redundant efforts, and are mostly not open source or using private datasets for training. This makes it challenging for users of various backgrounds to find and use the existing approaches.

For these reasons, this paper proposes a conceptual framework that describes and categorizes the tasks that need to be considered when estimating PV potential, thus creating a clear framework along which the contents of this research report can be classified. Addidionally the open source workflow PASSION is introduced, which integrates the assessment of geographical, technical and economic potentials of regions under consideration along with the calculation of surface areas, orientations and slopes of individual rooftop sections. It also includes the detection of obstacles and existing PV installations. It is based on a novel two-look approach, in which three independent models are deployed in parallel for the identification of rooftops, sections and superstructures. The three models show a mean Intersection Over Union (IoU) between classes of 0.847, 0.753 and 0.462 respectively, and more importantly show consistent results in non-selected real life samples.