Photovoltaic and rainwater harvesting assessment on rooftop has been studied extensively. Detailed methodologies are available over large study areas and designed to use data that are usually difficult and expensive to acquire. However, much less attention has been paid to the use of low-cost data for the estimation of photovoltaic parameters and rainwater collection in individual buildings. In this study, a workflow for extraction of geometrical information used in Photovoltaic and rainwater

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... rvesting potential estimation from UAV optical images used to estimate photovoltaic and rainwater harvesting potential is presented. The optical images captured by the DJI Phantom 4 Unmanned Aerial Vehicle (UAV) were used to compute a point cloud, using state of the art Structure from Motion (SfM) algorithms. The modeling of the roof planes was made based on the spatial relationships between points using a Delaunay triangulation. From the generated model, roof geometrical parameters such as area, slope, and orientation were extracted and compared with reference measurements of Light Detection And Ranging (LiDAR) of the same scene. Statistical results from the experiments show that the SfM and LiDAR extracted parameters are very similar. The geometric parameters derived from UAV optical images can be used to support the analysis of the photovoltaic and rainwater harvesting potential in individual buildings. This method has the advantage to achieve results through the combination of low-cost technologies for data acquisition and processing, resulting in an easily reproducible methodology.