Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-4523-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-4523-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
03 Jul 2026
Data description article |
| 03 Jul 2026
| 03 Jul 2026
Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches
Shujun Li
State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China
University of Chinese Academy of Sciences, Beijing, 100049, China
School of Environment, Tsinghua University, Beijing, 100084, China
Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
TianGong Think Tank, Research Institute for Environmental Innovation (Suzhou) Tsinghua, 215163, China
Yongze Song
School of Design and the Built Environment, Curtin University, Perth, Australia
Peng Wang
CORRESPONDING AUTHOR
State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China
University of Chinese Academy of Sciences, Beijing, 100049, China
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Short summary
Wind power plays a crucial role in the global transition to clean energy. Here, we developed an innovative approach that integrates public mapping resources and AI models to generate a comprehensive global inventory of onshore wind turbines. The resulting dataset documents 416 532 onshore wind turbine installations worldwide. As an open-access resource, this dataset can support sustainable renewable energy development and optimization.
Wind power plays a crucial role in the global transition to clean energy. Here, we developed an...
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