DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data

Hoeser, Thorsten; Feuerstein, Stefanie; Kuenzer, Claudia

doi:https://doi.org/10.5194/essd-14-4251-2022

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.

https://doi.org/10.5194/essd-14-4251-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 9

Data description paper

|

19 Sep 2022

Data description paper |

| 19 Sep 2022

DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data

Thorsten Hoeser, Stefanie Feuerstein, and Claudia Kuenzer

Data sets

DeepOWT: A global offshore wind turbine data set Thorsten Hoeser and Claudia Kuenzer https://doi.org/10.5281/zenodo.5933967

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.