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 paper
| 
19 Sep 2022
Data description paper |
| 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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Cited
17 citations as recorded by crossref.
- Global offshore wind turbine detection: a combined application of deep learning and Google earth engine S. Zhang et al. 10.1080/01431161.2024.2391587
- Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer 10.3390/rs16101675
- 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
- Remote sensing unveils the explosive growth of global offshore wind turbines K. Wang et al. 10.1016/j.rser.2023.114186
- 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
- The properties of the global offshore wind turbine fleet C. Jung & D. Schindler 10.1016/j.rser.2023.113667
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. 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. 10.1016/j.jclepro.2023.140415
- Future global offshore wind energy under climate change and advanced wind turbine technology C. Jung et al. 10.1016/j.enconman.2024.119075
- 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
- Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data F. Wang et al. 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. 10.1080/15481603.2024.2407389
- Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. 10.1109/JOE.2023.3319741
- 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
- Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. 10.3390/math12152347
- Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco J. Kriese et al. 10.3390/rs14174327
16 citations as recorded by crossref.
- Global offshore wind turbine detection: a combined application of deep learning and Google earth engine S. Zhang et al. 10.1080/01431161.2024.2391587
- Marine Infrastructure Detection with Satellite Data—A Review R. Spanier & C. Kuenzer 10.3390/rs16101675
- 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
- Remote sensing unveils the explosive growth of global offshore wind turbines K. Wang et al. 10.1016/j.rser.2023.114186
- 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
- The properties of the global offshore wind turbine fleet C. Jung & D. Schindler 10.1016/j.rser.2023.113667
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- Identifying wind turbines from multiresolution and multibackground remote sensing imagery Y. Zhai et al. 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. 10.1016/j.jclepro.2023.140415
- Future global offshore wind energy under climate change and advanced wind turbine technology C. Jung et al. 10.1016/j.enconman.2024.119075
- 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
- Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data F. Wang et al. 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. 10.1080/15481603.2024.2407389
- Automatic Geolocation and Measuring of Offshore Energy Infrastructure With Multimodal Satellite Data P. Ma et al. 10.1109/JOE.2023.3319741
- 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
- Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach K. Flores-Huamán et al. 10.3390/math12152347
Latest update: 21 Nov 2024
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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