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Rooftop photovoltaics have been acknowledged as a critical component in cities' efforts to reduce their reliance on fossil fuels and move towards energy sustainability. Identifying rooftop areas suitable for installing rooftop photovoltaics-referred to as utilizable areas-is essential for effective energy planning and developing policies related to renewable energies. Utilizable areas are greatly affected by the size, shape, superstructures of rooftops, and shadow effects. This study estimates utilizable areas and solar energy potential of rooftops by considering the mentioned factors. First, rooftops are extracted from LiDAR data by training PointNet++, a neural network architecture for processing 3D point clouds. The second step involves extracting planar segments of rooftops using a combination of clustering and region growing. Finally, utilizable areas of planar segments are identified by removing areas that do not have a suitable size and do not receive sufficient solar irradiation. Additionally, in this step, areas reserved for accessibility to photovoltaics are removed. According to the experimental results, the methods have a high success rate in rooftop extraction, plane segmentation, and, consequently, estimating utilizable areas for photovoltaics.