Global Offshore Wind Turbine Dataset
Affiliations
Offshore wind farms are widely adopted by coastal countries to obtain clean and green energy; their environmental impact has gained an increasing amount of attention. Although offshore wind farm datasets are commercially available via energy industries, records of the exact spatial distribution of individual wind turbines and their construction trajectories are rather incomplete, especially at the global level. Here, we construct a global remote sensing-based offshore wind turbine (OWT) database derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. We developed a percentile-based yearly SAR image collection reduction and autoadaptive threshold algorithm in the Google Earth Engine platform to identify the spatiotemporal distribution of global OWTs. By 2019, 6,924 wind turbines were constructed in 14 coastal nations. An algorithm performance analysis and validation were performed, and the extraction accuracies exceeded 99% using an independent validation dataset. This dataset could further our understanding of the environmental impact of OWTs and support effective marine spatial planning for sustainable development.
High-resolution gridded dataset of China's offshore wind potential and costs under technical change.
An K, Cai W, Lu X, Wang C Sci Data. 2025; 12(1):69.
PMID: 39809771 PMC: 11732974. DOI: 10.1038/s41597-025-04428-8.
Global potential for seaweed aquaculture on existing offshore infrastructure.
Ross F, Malerba M, Macreadie P Heliyon. 2025; 11(1):e41248.
PMID: 39801954 PMC: 11720895. DOI: 10.1016/j.heliyon.2024.e41248.
Endangered Black-faced Spoonbills alter migration across the Yellow Sea due to offshore wind farms.
Lai Y, Choi C, Lee K, Kwon I, Lin C, Gibson L Ecology. 2024; 106(1):e4485.
PMID: 39604060 PMC: 11733854. DOI: 10.1002/ecy.4485.
Wind turbine database for intelligent operation and maintenance strategies.
Marti-Puig P, Blanco-M A, Cusido J, Sole-Casals J Sci Data. 2024; 11(1):255.
PMID: 38424074 PMC: 10904773. DOI: 10.1038/s41597-024-03067-9.
Li C, Coolen J, Scherer L, Mogollon J, Braeckman U, Vanaverbeke J Environ Sci Technol. 2023; 57(16):6455-6464.
PMID: 37058594 PMC: 10134491. DOI: 10.1021/acs.est.2c07797.