45 citations as recorded by crossref.

1. Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer https://doi.org/10.3390/rs16101675
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3. The properties of the global offshore wind turbine fleet C. Jung & D. Schindler https://doi.org/10.1016/j.rser.2023.113667
4. Sentinel-1 for offshore wind energy application C. Hasager & K. Dimitriadou https://doi.org/10.1016/j.rse.2026.115369
5. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis M. Kleebauer et al. https://doi.org/10.3390/ijgi14060232
6. Assessing the impacts of offshore wind turbines on suspended sediments concentration in northern China coastal waters on the basis of Sentinel-1/2 Y. Hou et al. https://doi.org/10.1016/j.marpolbul.2026.119450
7. 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 https://doi.org/10.33070/etars.4.2023.04
8. Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data F. Wang et al. https://doi.org/10.1109/JSTARS.2024.3392695
9. Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning Q. Ding et al. https://doi.org/10.1080/15481603.2024.2407389
10. An innovative GCRC framework for the multispectral remote sensing identification of offshore wind turbines in complex shallow marine environments H. Jin et al. https://doi.org/10.1080/17538947.2026.2667081
11. Wind farm wake losses under future build-out scenarios S. Warder & M. Piggott https://doi.org/10.1016/j.weer.2026.100025
12. Arctic ice-wedge landscape mapping by CNN using a fusion of Radarsat constellation Mission and ArcticDEM M. Merchant et al. https://doi.org/10.1016/j.rse.2024.114052
13. Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
14. 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. https://doi.org/10.5194/wes-10-245-2025
15. Investigating Metocean Effects on Floating Offshore Wind Platform Positional Offset Using Sentinel-2 Imagery L. Filipe et al. https://doi.org/10.1109/ACCESS.2026.3662158
16. 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. https://doi.org/10.5194/essd-15-3547-2023
17. Offshore wind energy potential in Shandong Sea of China revealed by ERA5 reanalysis data and remote sensing L. Liu et al. https://doi.org/10.1016/j.jclepro.2024.142745
18. Seasonally Robust Offshore Wind Turbine Detection in Sentinel-2 Imagery Using Imaging Geometry-Aware Deep Learning X. Song & Z. Li https://doi.org/10.3390/rs17142482
19. Precipitation Conditions in Offshore Wind Farm Zones: Insights from Satellites and Weather Simulations T. Ivanova et al. https://doi.org/10.1088/1742-6596/3131/1/012005
20. Quantification of turbid wakes in offshore wind farms using satellite remote sensing E. Lecordier et al. https://doi.org/10.1016/j.scitotenv.2025.178814
21. OWT-DNet: A Timely and High-Accuracy End-to-End Offshore Wind Turbine Detection Network Based on Multimodal Remote Sensing Data S. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3665662
22. Widely used datasets of wind energy infrastructures can seriously underestimate onshore turbines in the Mediterranean J. Cerri et al. https://doi.org/10.1016/j.biocon.2024.110870
23. Mapping land- and offshore-based wind turbines in China in 2023 with Sentinel-2 satellite data T. He et al. https://doi.org/10.1016/j.rser.2025.115566
24. Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. https://doi.org/10.3390/math12152347
25. Global offshore wind turbine detection: a combined application of deep learning and Google earth engine S. Zhang et al. https://doi.org/10.1080/01431161.2024.2391587
26. The interannual variations of installed capacity for offshore wind turbines in China: estimations derived solely from remote sensing Q. Ding et al. https://doi.org/10.1080/10095020.2025.2496393
27. Quantifying Supply-Side Mitigation Strategies for Offshore Wind Energy Droughts on a Global Scale C. Jung & D. Schindler https://doi.org/10.3390/en19040955
28. Satellite reconstruction of offshore wind turbine development processes in mainland China: Spatiotemporal patterns, installed capacity, and carbon reduction implications H. Jin et al. https://doi.org/10.1016/j.jclepro.2026.148614
29. Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi https://doi.org/10.3390/jmse11101855
30. Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. https://doi.org/10.1016/j.jag.2023.103613
31. Deep learning-based monitoring of offshore wind turbines in Shandong Sea of China and their location analysis L. Liu et al. https://doi.org/10.1016/j.jclepro.2023.140415
32. From macro to micro: A multi-scale method for assessing coastal wind energy potential in China L. Deng et al. https://doi.org/10.1016/j.apenergy.2025.125729
33. Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods P. Yang et al. https://doi.org/10.3390/rs17050940
34. THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE П. Васько et al. https://doi.org/10.36296/1819-8058.2025.3(82).125-136
35. Large decommissioning cost burdens of offshore wind turbines across China’s ocean S. Li et al. https://doi.org/10.1016/j.checir.2026.100017
36. Substantially lower estimates in China’s offshore wind potential using farm-scale spatial modeling and wake effects S. Xu et al. https://doi.org/10.1038/s41467-026-68655-2
37. Semantic-to-Instance Segmentation of Time-Invariant Offshore Wind Farms Using Sentinel-1 Time Series and Time-Shift Augmentation O. de Carvalho et al. https://doi.org/10.3390/en18051127
38. Deep learning-based object detection of offshore platforms on Sentinel-1 imagery and the impact of synthetic training data R. Spanier et al. https://doi.org/10.1080/01431161.2026.2612908
39. Endangered Black‐faced Spoonbills alter migration across the Yellow Sea due to offshore wind farms Y. Lai et al. https://doi.org/10.1002/ecy.4485
40. Satellite observations and deep learning unveil the rapid expansion of offshore wind turbines in China L. Liu et al. https://doi.org/10.1016/j.resconrec.2025.108706
41. Future global offshore wind energy under climate change and advanced wind turbine technology C. Jung et al. https://doi.org/10.1016/j.enconman.2024.119075
42. Design-based inference and data integration allow the efficient estimation and mapping of onshore wind turbines presence across large spatial scales J. Cerri et al. https://doi.org/10.1016/j.jnc.2026.127339
43. Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. https://doi.org/10.1109/JOE.2023.3319741
44. YOLOv5_CDB: A Global Wind Turbine Detection Framework Integrating CBAM and DBSCAN Y. Fei et al. https://doi.org/10.3390/rs17081322
45. Precipitation response to wind farms represented in WRF over the Southern Bight of the North Sea T. Ivanova et al. https://doi.org/10.1088/1742-6596/3224/2/022033