19 Apr 2022
19 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

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

Thorsten Hoeser1, Stefanie Feuerstein1, and Claudia Kuenzer1,2 Thorsten Hoeser et al.
  • 1German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234, Wessling, Germany
  • 2Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, 97074, Wuerzburg, Germany

Abstract. Offshore wind energy is at the advent of a massive global expansion. Driven by carbon neutral alternatives for energy generation, offshore wind energy receives growing attention as a renewable energy source. Despite the large amount of unused wind energy capacities worldwide, offshore wind farms have to be integrated into already intensively used maritime economic areas. The optimal choice of offshore wind farm locations is as crucial as compatibility with other stakeholders while minimising ecological impacts. Thus, a spatiotemporal data set for offshore wind turbine deployment is necessary to involve all stakeholders and exchange knowledge during the upcoming massive expansion of offshore wind farms. To that end, we introduce the DeepOWT data set (global offshore wind turbines derived with deep learning; available at:  (Hoeser and Kuenzer, 2022), which provides 9,941 locations of offshore wind energy infrastructure along with their deployment stages on a global scale. DeepOWT is based on freely accessible Earth observation data from 2016 until 2021. The locations were derived from radar imagery of the Sentinel-1 mission by applying deep learning based object detection, trained on synthetic training examples. The entire deployment process is reported in a quarterly frequency and spatially contextualised for each single wind turbine location in a ready to use GIS format. Therewith, the DeepOWT data set can directly be used to enable spatial planning, environmental investigations and to optimise location decisions and the deployment process.

Thorsten Hoeser et al.

Status: open (until 25 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-115', Anonymous Referee #1, 17 May 2022 reply

Thorsten Hoeser et al.

Data sets

DeepOWT: A global offshore wind turbine data set Hoeser, Thorsten and Kuenzer, Claudia

Thorsten Hoeser et al.


Total article views: 399 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
322 69 8 399 7 6
  • HTML: 322
  • PDF: 69
  • XML: 8
  • Total: 399
  • BibTeX: 7
  • EndNote: 6
Views and downloads (calculated since 19 Apr 2022)
Cumulative views and downloads (calculated since 19 Apr 2022)

Viewed (geographical distribution)

Total article views: 391 (including HTML, PDF, and XML) Thereof 391 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 26 May 2022
Short summary
DeepOWT (deep learning derived global offshore wind turbines) 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 such as transformer stations. It is derived by applying deep learning based object detection on Sentinel-1 imagery.