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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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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