Vision Foundation Models and Rule-Based Approaches for Roof Surface Segmentation and Photovoltaic Potential Analysis in Urban Areas

This study presents two methods for rapidly and effectively determining the photovoltaic (PV) potential of building roofs in urban areas using aerial photographs and point cloud data. In the first method, the Segment Anything Model (SAM) and Contrastive Language Image Pre-Training (CLIP) models are used to detect roof surfaces and obstacles from aerial photographs. In the second method, the Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are employed to identify roof surfaces from Light Detection and Ranging (LiDAR) point clouds. Through the first proposed method, the performance of current deep learning approaches in 2.5D PV potential analysis is investigated, while the second approach examines the performance of 3D PV potential analysis compared to the 2D approach. In PV potential analysis, the Photovoltaic Geographical Information System (PVGIS) Application Programming Interface (API) was utilized. The analysis is conducted based on roof parameters obtained through both proposed methods. In building detection, the first approach achieved an Intersection over Union (IoU) score of 94.29%, whereas the second approach attained an IoU score of 91.23%.