IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v393y2025ics0306261925008670.html
   My bibliography  Save this article

PV Segmenter: A frequency-guided edge-aware network for distributed photovoltaic segmentation in remote sensing imagery

Author

Listed:
  • Wang, Siyuan
  • Shao, Zhenfeng
  • Hou, Dongyang
  • Cai, Bowen

Abstract

Accurate localization and sizing of distributed photovoltaic (PV) systems using remote sensing imagery are critical for assessing installed capacity and forecasting solar generation potential. However, existing PV extraction methods predominantly rely on spatial-domain learning strategies, which struggle to capture the complex boundaries and fine details of small-scale PV systems. In this paper, we propose PV Segmenter, a frequency-guided edge-aware network that employs frequency-domain learning to improve edge detection and pattern recognition in distributed PV systems. Specifically, a frequency-enhanced edge detection module is designed to leverage frequency-domain decoupling for the extraction of edge semantics related to PV boundaries. An edge-guided feature discrimination module subsequently injects edge cues into multi-level semantic features to refine structural semantic representation. Furthermore, a context-aware cross-layer fusion module is designed to preserve critical details of small PV panels, facilitating robust edge detection. Finally, we introduce an object-edge hybrid loss function with deep supervision that jointly optimizes PV object and edge features. Experimental results on two distributed PV datasets demonstrate that PV Segmenter improves the Intersection over Union (IoU) by 1.96 % to 9.61 % compared to nine benchmark methods. The proposed method shows promise for accurately identifying small-scale PV systems and effectively defining complex boundaries, offering a viable solution for renewable energy assessment and smart grid planning.

Suggested Citation

  • Wang, Siyuan & Shao, Zhenfeng & Hou, Dongyang & Cai, Bowen, 2025. "PV Segmenter: A frequency-guided edge-aware network for distributed photovoltaic segmentation in remote sensing imagery," Applied Energy, Elsevier, vol. 393(C).
    • Handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008670
    DOI: 10.1016/j.apenergy.2025.126137
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925008670
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.126137?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Lu, Ning & Li, Liang & Qin, Jun, 2024. "PV Identifier: Extraction of small-scale distributed photovoltaics in complex environments from high spatial resolution remote sensing images," Applied Energy, Elsevier, vol. 365(C).
    3. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    4. 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.
    5. Chen, Di & Peng, Qiuzhi & Lu, Jiating & Huang, Peiyi & Song, Yufei & Peng, Fengcan, 2024. "Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data," Applied Energy, Elsevier, vol. 376(PB).
    6. Guo, Zhiling & Lu, Jiayue & Chen, Qi & Liu, Zhengguang & Song, Chenchen & Tan, Hongjun & Zhang, Haoran & Yan, Jinyue, 2024. "TransPV: Refining photovoltaic panel detection accuracy through a vision transformer-based deep learning model," Applied Energy, Elsevier, vol. 355(C).
    7. 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).
    8. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    9. 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).
    10. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    11. Yang, Ruiqing & He, Guojin & Yin, Ranyu & Wang, Guizhou & Zhang, Zhaoming & Long, Tengfei & Peng, Yan, 2024. "Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map," Applied Energy, Elsevier, vol. 361(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Bouaziz, Mohamed Chahine & El Koundi, Mourad & Ennine, Ghaleb, 2024. "High-resolution solar panel detection in Sfax, Tunisia: A UNet-Based approach," Renewable Energy, Elsevier, vol. 235(C).
    3. Yang, Ruiqing & He, Guojin & Yin, Ranyu & Wang, Guizhou & Peng, Xueli & Zhang, Zhaoming & Long, Tengfei & Peng, Yan & Wang, Jianping, 2025. "A large-scale ultra-high-resolution segmentation dataset augmentation framework for photovoltaic panels in photovoltaic power plants based on priori knowledge," Applied Energy, Elsevier, vol. 390(C).
    4. Thebault, Martin & Nerot, Boris & Govehovitch, Benjamin & Ménézo, Christophe, 2025. "A comprehensive building-wise rooftop photovoltaic system detection in heterogeneous urban and rural areas: application to French territories," Applied Energy, Elsevier, vol. 388(C).
    5. 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).
    6. Tan, Hongjun & Guo, Zhiling & Lin, Zhengyuan & Chen, Yuntian & Huang, Dou & Yuan, Wei & Zhang, Haoran & Yan, Jinyue, 2024. "General generative AI-based image augmentation method for robust rooftop PV segmentation," Applied Energy, Elsevier, vol. 368(C).
    7. Chen, Di & Peng, Qiuzhi & Lu, Jiating & Huang, Peiyi & Song, Yufei & Peng, Fengcan, 2024. "Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data," Applied Energy, Elsevier, vol. 376(PB).
    8. Yang, Ruiqing & He, Guojin & Yin, Ranyu & Wang, Guizhou & Zhang, Zhaoming & Long, Tengfei & Peng, Yan, 2024. "Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map," Applied Energy, Elsevier, vol. 361(C).
    9. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    10. Salim, Daniel Henrique Carneiro & de Sousa Mello, Caio César & Franco, Guilherme Gandra & de Albuquerque Nóbrega, Rodrigo Affonso & de Paula, Eduardo Coutinho & Fonseca, Bráulio Magalhães & Nero, Marc, 2023. "Unveiling Fernando de Noronha Island's photovoltaic potential with unmanned aerial survey and irradiation modeling," Applied Energy, Elsevier, vol. 337(C).
    11. Guo, Zhiling & Zhuang, Zhan & Tan, Hongjun & Liu, Zhengguang & Li, Peiran & Lin, Zhengyuan & Shang, Wen-Long & Zhang, Haoran & Yan, Jinyue, 2023. "Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets," Renewable Energy, Elsevier, vol. 219(P1).
    12. Li, Guanglei & Wang, Guohao & Luo, Tengqi & Hu, Yuxiao & Wu, Shouyuan & Gong, Guanghui & Song, Chenchen & Guo, Zhiling & Liu, Zhengguang, 2024. "SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city," Renewable Energy, Elsevier, vol. 237(PA).
    13. Yang, Jinhao & Wu, Jinghua & Lu, Junjie & Peng, Xiangang & Yuan, Haoliang & Lai, Loi Lei, 2024. "A novel method for assessment rooftop PV potential based on remote sensing images," Renewable Energy, Elsevier, vol. 237(PC).
    14. Cardoso, Andressa & Jurado-Rodríguez, David & López, Alfonso & Ramos, M. Isabel & Jurado, Juan Manuel, 2024. "Automated detection and tracking of photovoltaic modules from 3D remote sensing data," Applied Energy, Elsevier, vol. 367(C).
    15. Gabriel Kasmi & Augustin Touron & Philippe Blanc & Yves-Marie Saint-Drenan & Maxime Fortin & Laurent Dubus, 2024. "Remote-Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data," Energies, MDPI, vol. 17(17), pages 1-22, August.
    16. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    17. Justinas Lekavičius & Valentas Gružauskas, 2024. "Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images," Energies, MDPI, vol. 17(13), pages 1-20, June.
    18. 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).
    19. Wang, Bo & Chen, Qi & Wang, Mengmeng & Chen, Yuntian & Zhang, Zhengjia & Liu, Xiuguo & Gao, Wei & Zhang, Yanzhen & Zhang, Haoran, 2024. "PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification," Applied Energy, Elsevier, vol. 376(PA).
    20. Xiao Ma & Yongchun Yang & Huazhang Zhu, 2025. "Spatiotemporal Characteristics and Influencing Factors of Renewable Energy Production Development in Ningxia Hui Autonomous Region, China (2014–2021)," Land, MDPI, vol. 14(4), pages 1-26, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:393:y:2025:i:c:s0306261925008670. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.