Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery

• A novel edge-enhanced SAM model is proposed to improve PV power plant extraction. • An edge module is developed to enhance the edge delineation of PV power plants. • A learning-based fusion module is designed to combine semantic and edge features. • The model performs well in PV power plant extraction with an OA of 98.46%. Accurate geospatial extent data of photovoltaic (PV) power plants is essential for assessing their socioeconomic benefits and environmental impacts. However, existing semantic segmentation models often result in over-smoothed edges and the inaccurate delineation of PV power plants. To address these limitations, we proposed a novel edge-enhanced Segment Anything Model (ESAM) tailored for PV power plant extraction. It is designed as a multi-task network that integrates three key components: semantic segmentation, edge detection, and semantic and edge fusion. The semantic model leverages a modified Segment Anything Model (SAM) foundation model to extract semantic features of PV power plants. An edge module is developed to improve the edge delineation ability of ESAM. Additionally, a learning-based fusion module is designed to combine semantic and edge information to enhance PV power plant identifications. Validation demonstrates that ESAM achieved a high overall accuracy (OA = 98.46 %). Meanwhile, it outperformed three state-of-the-art models by providing higher accuracy metrics and more accurate edges of PV power plants. Thus, the proposed ESAM offers a robust tool for PV power plant extraction.