27 citations as recorded by crossref.

1. Global offshore wind turbine detection: a combined application of deep learning and Google earth engine S. Zhang et al. 10.1080/01431161.2024.2391587
2. The interannual variations of installed capacity for offshore wind turbines in China: estimations derived solely from remote sensing Q. Ding et al. 10.1080/10095020.2025.2496393
3. Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer 10.3390/rs16101675
4. Remote sensing unveils the explosive growth of global offshore wind turbines K. Wang et al. 10.1016/j.rser.2023.114186
5. The properties of the global offshore wind turbine fleet C. Jung & D. Schindler 10.1016/j.rser.2023.113667
6. Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
7. Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. 10.1016/j.jag.2023.103613
8. Deep learning-based monitoring of offshore wind turbines in Shandong Sea of China and their location analysis L. Liu et al. 10.1016/j.jclepro.2023.140415
9. From macro to micro: A multi-scale method for assessing coastal wind energy potential in China L. Deng et al. 10.1016/j.apenergy.2025.125729
10. EXPRESS ANALYSIS OF PROBABILISTIC CHARACTERISTICS OF WIND POWER TATIONS AS A SOURCE OF ENERGY FOR SEAWATER DESALINATION IN THE AZOV-BLACK SEA REGION OF UKRAINE P. Vasko & I. Mazurenko 10.33070/etars.4.2023.04
11. Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data F. Wang et al. 10.1109/JSTARS.2024.3392695
12. Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning Q. Ding et al. 10.1080/15481603.2024.2407389
13. Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods P. Yang et al. 10.3390/rs17050940
14. Arctic ice-wedge landscape mapping by CNN using a fusion of Radarsat constellation Mission and ArcticDEM M. Merchant et al. 10.1016/j.rse.2024.114052
15. Semantic-to-Instance Segmentation of Time-Invariant Offshore Wind Farms Using Sentinel-1 Time Series and Time-Shift Augmentation O. de Carvalho et al. 10.3390/en18051127
16. Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms T. Ivanova et al. 10.5194/wes-10-245-2025
17. China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery Z. Liu et al. 10.5194/essd-15-3547-2023
18. Endangered Black‐faced Spoonbills alter migration across the Yellow Sea due to offshore wind farms Y. Lai et al. 10.1002/ecy.4485
19. Offshore wind energy potential in Shandong Sea of China revealed by ERA5 reanalysis data and remote sensing L. Liu et al. 10.1016/j.jclepro.2024.142745
20. Future global offshore wind energy under climate change and advanced wind turbine technology C. Jung et al. 10.1016/j.enconman.2024.119075
21. Quantification of turbid wakes in offshore wind farms using satellite remote sensing E. Lecordier et al. 10.1016/j.scitotenv.2025.178814
22. Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. 10.1109/JOE.2023.3319741
23. Widely used datasets of wind energy infrastructures can seriously underestimate onshore turbines in the Mediterranean J. Cerri et al. 10.1016/j.biocon.2024.110870
24. YOLOv5_CDB: A Global Wind Turbine Detection Framework Integrating CBAM and DBSCAN Y. Fei et al. 10.3390/rs17081322
25. Mapping land- and offshore-based wind turbines in China in 2023 with Sentinel-2 satellite data T. He et al. 10.1016/j.rser.2025.115566
26. Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. 10.3390/math12152347
27. Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco J. Kriese et al. 10.3390/rs14174327