Assessment of Wuhan's rooftop photovoltaic geographic potential based on deep learning

doi:10.13474/j.cnki.11-2246.2026.0725

Abstract

Abstract: [Purposes] This study aims to accurately assess the rooftop photovoltaic (PV)installation potential in Wuhan and support urban distributed PV planning. [Methods] This study integrates Wuhan's manually labeled dataset and public datasets for model training,systematically compares multiple deep learning semantic segmentation models to determine the optimal solution,completes rooftop classification based on urban functional zone planning,and calculates the installed capacity with availability coefficients. [Findings] The results show that the optimal model achieves a rooftop segmentation accuracy of over 96%,identifies five types of rooftop resources in Wuhan.The city's total rooftop area is 522 km²,and the rooftop PV installed capacity reaches 38 554.64 MW,presenting a pattern of “high density in core areas and large total capacity in outer suburbs”. [Conclusions] This study realizes the accurate quantification of Wuhan's rooftop PV geographic potential through the construction of a dataset adapted to local conditions in Wuhan,model selection,and quantitative evaluation of classified potential,providing a scientific basis for the layout and development of distributed PV in Wuhan.

Key words: roof extraction, deep learning, high-resolution imagery, photovoltaic potential assessment, remote sensing image recognition

CLC Number:

P23
P208

Liu Weili, Tian Zhiyong, Luo Yongqiang. Assessment of Wuhan's rooftop photovoltaic geographic potential based on deep learning[J]. Bulletin of Surveying and Mapping, 2026, 0(7): 167-172.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.0725

http://tb.chinasmp.com/EN/Y2026/V0/I7/167

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