Castello, RobertoWalch, AlinaAttias, RaphaelCadei, RiccardoJiang, ShashaScartezzini, Jean-Louis2022-01-012022-01-012022-01-012021-01-0110.1088/1742-6596/2042/1/012002https://infoscience.epfl.ch/handle/20.500.14299/184088WOS:000724676100002The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.Construction & Building TechnologyGreen & Sustainable Science & TechnologyEnergy & FuelsRegional & Urban PlanningScience & Technology - Other TopicsPublic Administrationtext::conference output::conference proceedings::conference paper