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Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches
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DOI：10.5194/essd-18-4523-2026.png)
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Abstract
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Abstract. Wind power serves as a vital zero-carbon alternative to fossil fuels for climate change mitigation. Nevertheless; the vast expansion of wind turbine installation requires extensive terrestrial resources; raising wide concerns regarding land use competition and ecological impacts. Quantifying these effects necessitates near real-time geospatial data on wind turbine placement and density. However; current methods remain inadequate for monitoring the fast-growing wind turbine deployment. Here; we developed an integrated framework that combines OpenStreetMap (OSM) data with multi-source remote sensing images (Google Earth and Sentinel-1/2); and deep learning and traditional machine learning models (ResNet-18 and Random Forest) to map global onshore wind turbines. Our models achieve validation accuracy > 97 % while enabling cost-effective; timely updates of global onshore wind turbines. Eventually; we established a geographical dataset (GonshoreWT2024) covering a total of 416 532 wind turbines globally by 2024. This dataset represents a tenfold expansion over global wind turbine inventories as of 2020; and updates 42 955 more onshore wind turbines compared to the Global Renewables Watch based on lower computational requirements. In addition; we found that 87 % of wind turbines are situated on cropland and grassland; followed by forest and bare ground. This dataset facilitates essential studies on renewable energy land management; ecological impact analysis; and data-driven energy transition policies. The codes and dataset of the global onshore wind turbines are available at the Zenodo link: https://doi.org/10.5281/zenodo.18984175 (Shujun et al.; 2026).
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