Offshore oil and gas platform dynamics in the North Sea, Gulf of Mexico, and Persian Gulf: Exploiting the Sentinel-1 archive
Robin Spanier, Thorsten Hoeser, John Truckenbrodt, Felix Bachofer, Claudia Kuenzer

TL;DR
This paper presents an automated method using Sentinel-1 data and deep learning to monitor offshore oil and gas platforms across three major regions from 2017 to 2025, revealing regional trends and infrastructure dynamics.
Contribution
It introduces a novel, scalable approach for systematic offshore platform detection and monitoring using freely available satellite data and deep learning techniques.
Findings
Identified 3,728 platforms globally in 2025.
Observed regional variations in platform numbers and activity.
Highlighted the increasing mobility and short lifespan of offshore platforms.
Abstract
The increasing use of marine spaces by offshore infrastructure, including oil and gas platforms, underscores the need for consistent, scalable monitoring. Offshore development has economic, environmental, and regulatory implications, yet maritime areas remain difficult to monitor systematically due to their inaccessibility and spatial extent. This study presents an automated approach to the spatiotemporal detection of offshore oil and gas platforms based on freely available Earth observation data. Leveraging Sentinel-1 archive data and deep learning-based object detection, a consistent quarterly time series of platform locations for three major production regions: the North Sea, the Gulf of Mexico, and the Persian Gulf, was created for the period 2017-2025. In addition, platform size, water depth, distance to the coast, national affiliation, and installation and decommissioning dates…
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Taxonomy
TopicsMarine and Offshore Engineering Studies · Oil Spill Detection and Mitigation · Maritime Ports and Logistics
