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

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 monitoring for 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 covering a total of 379,595 wind turbines globally by 2024. This dataset represents a tenfold expansion over currently available global wind turbine inventories as of 2020. In addition, we found that 80% 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 is available at Zenodo link: https://doi.org/10.5281/zenodo.16759861 (Shujun et al., 2025).