Mapping global onshore wind turbines using multi-source remote sensing images  and hybrid learning approaches

Li, Shujun; Qi, Jianchuan; Song, Yongze; Wang, Peng

doi:10.5194/essd-18-4523-2026

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.

https://doi.org/10.5194/essd-18-4523-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 7

Data description article

|

03 Jul 2026

Data description article |

| 03 Jul 2026

Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches

Shujun Li, Jianchuan Qi, Yongze Song, and Peng Wang

Data sets

Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches Shujun Li et al. https://doi.org/10.5281/zenodo.18984175

Model code and software

Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches Shujun Li et al. https://doi.org/10.5281/zenodo.18984175

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.