Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002274 |
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| author | Yuehong Chen Jiayue Zhou Yu Chen Jiawei Wang Xiaoxiang Zhang Yong Ge Hongyuan Ma |
| author_facet | Yuehong Chen Jiayue Zhou Yu Chen Jiawei Wang Xiaoxiang Zhang Yong Ge Hongyuan Ma |
| author_sort | Yuehong Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d409124bb9d7454e9e61126ad5d3dee2 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-d409124bb9d7454e9e61126ad5d3dee22025-08-20T02:34:31ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010458010.1016/j.jag.2025.104580Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imageryYuehong Chen0Jiayue Zhou1Yu Chen2Jiawei Wang3Xiaoxiang Zhang4Yong Ge5Hongyuan Ma6Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, ChinaJiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, ChinaSchool of Geography, Nanjing Normal University, Nanjing 210023, China; Corresponding authors.Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, ChinaJiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China; Corresponding authors.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaQinghai Huanghe Hydropower Development Co., LTD, Xining 810008, ChinaAccurate 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.http://www.sciencedirect.com/science/article/pii/S1569843225002274PV power plantsSemantic segmentationEdge detectionLearning-based FusionSAM |
| spellingShingle | Yuehong Chen Jiayue Zhou Yu Chen Jiawei Wang Xiaoxiang Zhang Yong Ge Hongyuan Ma Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery International Journal of Applied Earth Observations and Geoinformation PV power plants Semantic segmentation Edge detection Learning-based Fusion SAM |
| title | Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery |
| title_full | Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery |
| title_fullStr | Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery |
| title_full_unstemmed | Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery |
| title_short | Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery |
| title_sort | edge enhanced sam for extracting photovoltaic power plants from remote sensing imagery |
| topic | PV power plants Semantic segmentation Edge detection Learning-based Fusion SAM |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002274 |
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