Published February 22, 2026
| Version 3.25.1
Dataset
Open
DeepOWT v3.25.1: Dense Sentinel-1 Time Series for Deployment and Operational Dynamics
Authors/Creators
- 1. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR)
-
2.
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
- 3. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR); Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg
Description
DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale.
Locations are derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive.
Dense time series are derived by inspecting each available Sentinel-1 scene at every detected infrastructure location. One-dimensional swath profiles are generated for each acquisition and infrastructure location, showing the maximum SAR backscatter value along the horizontal axis, thereby capturing directed SAR signatures.
File metadata
| File(s) | Time | Geometry | Spatial extent | Temporal resolution |
|---|---|---|---|---|
| DeepOWT.parquet (Derived Locations) | 2016Q1-2025Q1 | points | Global | quarterly |
| location_validation_2025Q1.parquet (Ground Truth Location) | 2025Q1 | polygons | North Sea Basin, East China Sea, Southeast Vietnam | - |
| swath_profile_time_series/part_*_swath_profile_time_series.parquet (Analysis Ready Time Series, and Derived Baseline Labels) | 2016Q1-2025Q1 | - (related to DeepOWT points via unit_id) | - | for each available S1-acquisition (~2-12 days) |
| swath_profile_time_series_validation.parquet (Ground Truth Time Series Event Labels) | 2016Q1-2025Q1 | - (related to DeepOWT points via unit_id) | - | for each available S1-acquisition (~2-12 days) |
Files
swath_profile_time_series.zip
Additional details
Dates
- Submitted
-
2026-02-23