Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4251-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
19 Sep 2022
Data description article |
| 19 Sep 2022
| 19 Sep 2022
DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany
Stefanie Feuerstein
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany
Claudia Kuenzer
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany
Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, 97074 Würzburg, Germany
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44 citations as recorded by crossref.
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- 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
- Quantifying Supply-Side Mitigation Strategies for Offshore Wind Energy Droughts on a Global Scale C. Jung & D. Schindler https://doi.org/10.3390/en19040955
- Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer https://doi.org/10.3390/rs16101675
- Remote sensing unveils the explosive growth of global offshore wind turbines K. Wang et al. https://doi.org/10.1016/j.rser.2023.114186
- The properties of the global offshore wind turbine fleet C. Jung & D. Schindler https://doi.org/10.1016/j.rser.2023.113667
- Sentinel-1 for offshore wind energy application C. Hasager & K. Dimitriadou https://doi.org/10.1016/j.rse.2026.115369
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi https://doi.org/10.3390/jmse11101855
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods P. Yang et al. https://doi.org/10.3390/rs17050940
- 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
- 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
- Wind farm wake losses under future build-out scenarios S. Warder & M. Piggott https://doi.org/10.1016/j.weer.2026.100025
- 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
- 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
- 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
- Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. https://doi.org/10.1109/JOE.2023.3319741
- 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
- YOLOv5_CDB: A Global Wind Turbine Detection Framework Integrating CBAM and DBSCAN Y. Fei et al. https://doi.org/10.3390/rs17081322
- 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
- Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. https://doi.org/10.3390/math12152347
- 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
44 citations as recorded by crossref.
- 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
- 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
- Quantifying Supply-Side Mitigation Strategies for Offshore Wind Energy Droughts on a Global Scale C. Jung & D. Schindler https://doi.org/10.3390/en19040955
- Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer https://doi.org/10.3390/rs16101675
- Remote sensing unveils the explosive growth of global offshore wind turbines K. Wang et al. https://doi.org/10.1016/j.rser.2023.114186
- The properties of the global offshore wind turbine fleet C. Jung & D. Schindler https://doi.org/10.1016/j.rser.2023.113667
- Sentinel-1 for offshore wind energy application C. Hasager & K. Dimitriadou https://doi.org/10.1016/j.rse.2026.115369
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi https://doi.org/10.3390/jmse11101855
- Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. https://doi.org/10.1016/j.jag.2023.103613
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods P. Yang et al. https://doi.org/10.3390/rs17050940
- 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
- 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
- Wind farm wake losses under future build-out scenarios S. Warder & M. Piggott https://doi.org/10.1016/j.weer.2026.100025
- 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
- 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
- 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
- Copernicus Data for Offshore Wind Energy: Capabilities, Applications and Emerging Trends P. Poozesh et al. https://doi.org/10.3390/su18041949
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. https://doi.org/10.1109/JOE.2023.3319741
- 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
- YOLOv5_CDB: A Global Wind Turbine Detection Framework Integrating CBAM and DBSCAN Y. Fei et al. https://doi.org/10.3390/rs17081322
- 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
- Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. https://doi.org/10.3390/math12152347
- 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
Saved (final revised paper)
Latest update: 01 Jun 2026
Short summary
The DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set provides offshore wind energy infrastructure locations and their temporal deployment dynamics from July 2016 until June 2021 on a global scale. It differentiates between offshore wind turbines, platforms under construction, and offshore wind farm substations. It is derived by applying deep-learning-based object detection to Sentinel-1 imagery.
The DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set provides offshore wind...
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