Wind turbine extraction utilizing high-resolution satellite imagery

Fuqiang Liu; Xiaopan Chen; Yongtian Shen; Song Huang; Qing Wei; Shiju Chang; Likui Wang; Xiao Hou; Zhipeng Chen; Jianwei Yu; Zhe Zeng

doi:10.1117/12.3021132

19 January 2024 Wind turbine extraction utilizing high-resolution satellite imagery

Fuqiang Liu, Xiaopan Chen, Yongtian Shen, Song Huang, Qing Wei, Shiju Chang, Likui Wang, Xiao Hou, Zhipeng Chen, Jianwei Yu, Zhe Zeng

Author Affiliations +

Proceedings Volume 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023); 129801A (2024) https://doi.org/10.1117/12.3021132
Event: Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 2023, Lianyungang, China

Abstract

To achieve the objective of reducing carbon emissions, countries worldwide are actively advancing the development of new energy sources, including wind power. This article introduces an enhanced method that utilizes deep neural networks based on YOLOv5 to identify and extract wind turbines from remote-sensing imagery by analysing high resolution remote-sensing images captured by multispectral sensors on satellites.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fuqiang Liu, Xiaopan Chen, Yongtian Shen, Song Huang, Qing Wei, Shiju Chang, Likui Wang, Xiao Hou, Zhipeng Chen, Jianwei Yu, and Zhe Zeng "Wind turbine extraction utilizing high-resolution satellite imagery", Proc. SPIE 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 129801A (19 January 2024); https://doi.org/10.1117/12.3021132

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