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A deep-learning multi-source information fusion method for high-precision PV identification: Integration of U2-net image segmentation and multi-spectral screening

Author

Listed:
  • Yang, Junyi
  • Zhao, Lihua
  • Xu, Chengliang
  • Sun, Yongjun
  • Ren, Haoshan
  • Nie, Zichuan

Abstract

Accurate photovoltaic (PV) identification offers a promising prospect for site selection and wide penetration of future PV systems. This study was dedicated to enhancing the precision of PV identification techniques within urban environments through the integration of the U2-Net neural network image segmentation model and the multi-spectral PV screening technique. The U2-Net model conducted the image segmentation on visible light satellite images to obtain coordinates and areas of existing PV sites. The multi-spectral screening technique then processed the image segmentation results with multi-spectral satellite images to screen out misidentified samples. The Photovoltaic Index (PVI) and its normalized expression (nPVI) were established to improve the screening performance of the technique. A detailed case study showed that the proposed deep-learning multi-source information fusion method achieved high accuracy PV identification with the intersection over union (IoU) and precision increased by 7.13 % and 8.66 %, respectively, reaching 91.37 % and 93.86 %. This was because the false positive samples of the U2-Net image segmentation model were effectively filtered out by the multi-spectral screening technique. The PV deployments in the three megacities (i.e., Guangzhou, Shenzhen, and Dongguan) of Guangdong Province were identified using the developed method. The results showed that the distribution of PV samples is denser in areas with a higher level of industrial development and land resources, while in urban core areas with high urbanization density and limited spatial resources, the distribution of PV samples is sparser. In conclusion, the proposed method has the potential to enable accurate PV identification by efficiently harnessing multi-spectral data. It can assist in formulating more targeted PV deployment strategies, guiding the rational allocation of PV installation resources in various urban contexts, and promoting the integration of PV systems with urban planning, thereby contributing to the global advancement of renewable energy development and the realization of sustainable urban energy transitions.

Suggested Citation

  • Yang, Junyi & Zhao, Lihua & Xu, Chengliang & Sun, Yongjun & Ren, Haoshan & Nie, Zichuan, 2025. "A deep-learning multi-source information fusion method for high-precision PV identification: Integration of U2-net image segmentation and multi-spectral screening," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925012784
    DOI: 10.1016/j.apenergy.2025.126548
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    1. Hui, Tan Kah & Hwee, Ah Khoon & QingHua, Liu & Jing, Khoo Terh & Ohueri, Chukwuka Christian, 2026. "From state-of-the-art to next-generation solutions: A review for Gen-AI integration in photovoltaic maintenance," Applied Energy, Elsevier, vol. 402(PB).

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