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Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI

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  • Rui Zhang

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Ruikai Hong

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Qiannan Li

    (Henan Provincial Key Laboratory of Ecological Environment Remote Sensing, Zhengzhou 450046, China)

  • Xu He

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Age Shama

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Jichao Lv

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Renzhe Wu

    (Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

Photovoltaic (PV) technology, as a crucial source of clean energy, can effectively mitigate the impact of climate change caused by fossil fuel-based power generation. However, improper use of PV installations may encroach upon agricultural land, grasslands, and other land uses, thereby affecting local ecosystems. Exploring the spatial characteristics of centralized or distributed PV installations is essential for quantifying the development of clean energy and protecting agricultural land. Due to the distinct characteristics of centralized and distributed PV installations, large-scale mapping methods based on satellite remote sensing are insufficient for creating detailed PV distribution maps. This study proposes a model called Joint Semi-Supervised Weighted Adaptive PV Panel Recognition Model (JSWPVI)to achieve reliable PV mapping using UAV datasets. The JSWPVI employs a semi-supervised approach to construct and optimize a comprehensive segmentation network, incorporating the Spatial and Channel Weight Adaptive Model (SCWA) module to integrate different feature layers by reconstructing the spatial and channel weights of feature maps. Finally, a guided filtering algorithm is used to minimize non-edge noise while preserving edge integrity. Our results demonstrate that JSWPVI can accurately extract PV panels in both centralized and distributed scenarios, with an average extraction accuracy of 91.1% and a mean Intersection over Union of 77.7%. The findings of this study will assist regional policymakers in better quantifying renewable energy potential and assessing environmental impacts.

Suggested Citation

  • Rui Zhang & Ruikai Hong & Qiannan Li & Xu He & Age Shama & Jichao Lv & Renzhe Wu, 2025. "Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI," Land, MDPI, vol. 14(6), pages 1-20, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:6:p:1245-:d:1675853
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    References listed on IDEAS

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    1. Li, Liang & Lu, Ning & Qin, Jun, 2025. "Joint-task learning framework with scale adaptive and position guidance modules for improved household rooftop photovoltaic segmentation in remote sensing image," Applied Energy, Elsevier, vol. 377(PB).
    2. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    3. Qiu, Tianzhi & Wang, Lunche & Lu, Yunbo & Zhang, Ming & Qin, Wenmin & Wang, Shaoqiang & Wang, Lizhe, 2022. "Potential assessment of photovoltaic power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    4. Marcus Vinícius Coelho Vieira da Costa & Osmar Luiz Ferreira de Carvalho & Alex Gois Orlandi & Issao Hirata & Anesmar Olino de Albuquerque & Felipe Vilarinho e Silva & Renato Fontes Guimarães & Robert, 2021. "Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation," Energies, MDPI, vol. 14(10), pages 1-15, May.
    5. Tan, Hongjun & Guo, Zhiling & Zhang, Haoran & Chen, Qi & Lin, Zhenjia & Chen, Yuntian & Yan, Jinyue, 2023. "Enhancing PV panel segmentation in remote sensing images with constraint refinement modules," Applied Energy, Elsevier, vol. 350(C).
    6. L. Kruitwagen & K. T. Story & J. Friedrich & L. Byers & S. Skillman & C. Hepburn, 2021. "A global inventory of photovoltaic solar energy generating units," Nature, Nature, vol. 598(7882), pages 604-610, October.
    7. Mayer, Kevin & Rausch, Benjamin & Arlt, Marie-Louise & Gust, Gunther & Wang, Zhecheng & Neumann, Dirk & Rajagopal, Ram, 2022. "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D," Applied Energy, Elsevier, vol. 310(C).
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