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Published March 16, 2026 | Version 1.0.0

OPD v1.0.0: Sentinel-1 derived dataset of offshore oil and gas platform locations, lifespans, and spatial attributes

  • 1. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR)
  • 2. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR); Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg

Description

The Offshore Platform Dataset (OPD) provides geospatial locations, temporal deployment information, and spatial attributes of offshore oil and gas platforms derived from Sentinel-1 satellite imagery.

Platform locations were detected using a deep learning–based object detection approach applied to the Sentinel-1 archive. By analysing dense Sentinal-1 time series, quarterly platform presence and lifespan information were derived for the period 2017Q1-2025Q1. Additional spatial attributes include water depth, distance to coast, exclusive economic zones (EEZ) affiliation, and bounding box area. The dataset currently covers three major offshore production regions: the North Sea, Gulf of Mexico, and Persian Gulf. 

File(s) Description Key attributes Time coverage

OPD_25Q1.geojson

OPD_25Q1.parquet

Offshore platforms present in 2025Q1.
  • installed_quarter
  • country_eez
  • distance_to_coast_km
  • water_depth_m
  • detection_bbox_area_ha
2025Q1

OPD_platforms.geojson

OPD_platforms.parquet

Unique offshore platforms between 2017Q1-2025Q1.
  • installed_quarter
  • last_observed_quarter
  • country_eez
  • distance_to_coast_km
  • water_depth_m
  • detection_bbox_area_ha
2017Q1-2025Q1

OPD_platforms_per_quarter.geojson

OPD_platforms_per_quarter.parquet

Offshore platforms for each quarter between 2017Q1-2025Q1.
  • quarter
  • installed_quarter
  • last_observed_quarter
  • country_eez
  • distance_to_coast_km
  • water_depth_m
  • detection_bbox_area_ha
2017Q1-2025Q1 (quarterly)

Files

OPD_25Q1.geojson

Files (50.2 MB)

Name Size
md5:98ef88250e0e1cf51162229c805d3913
1.3 MB Preview Download
md5:fec2184bac7748df8ddaed92f47295e1
189.4 kB Download
md5:061ca8ab3da77fe48bfba52082e142b3
2.6 MB Preview Download
md5:13843671125c57ba67c8dabbff7272de
322.5 kB Download
md5:1bfc12ae41e6c5a960487a14c3704a12
45.3 MB Preview Download
md5:37f00a8b491177407e2feda1421319b6
435.0 kB Download

Additional details

Related works

Is derived from
Journal article: 10.1080/01431161.2026.2612908 (DOI)
Is described by
Preprint: 10.48550/arXiv.2603.19801 (DOI)