Automatic geolocation and measuring of offshore energy infrastructure with multimodal satellite data.

Ma, Ping; Macdonald, Malcolm; Rouse, Sally; Ren, Jinchang

Abstract

With the increasing trend of energy transition to low-carbon economies, the rate of offshore structure installation and removal will rapidly accelerate through offshore renewable energy development and oil and gas decommissioning. Knowledge of the location and size of offshore infrastructure is vital in the management of marine ecosystems and also for safe navigation at sea. The availability of multimodal data enables the systematic assessment of offshore infrastructure. In this article, we propose an automatic solution for the geolocation and size evaluation of offshore infrastructure through a data fusion model of Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 multispectral instrument imagery. The use of the Sentinel-1 (SAR) data aims to quickly localize candidate offshore energy infrastructure with its all-weather imaging capabilities, while the high-resolution optical data provided by the Sentinel-2 can enable more accurate localization and measurement of the offshore infrastructure. To be specific, a candidate detection model is applied to a time series of Sentinel-1 images to extract the "guided area" of the infrastructure, followed by morphological operation-based precise localization within an individual Sentinel-2 image, as well as estimating the size of each structure. With validation against the ground truth data of the Scottish waters from the baseline and closing bays, to the limit of the exclusive economic zone of Scotland, an area of 371 915 km2 , our method has automatically identified 332 objects with an omission error of 0.3% and a commission rate of 0%. Our method was comprehensively compared with two state-of-the-art offshore energy infrastructure detection algorithms. The results validate that our method achieves the highest overall accuracy of 99.70%, surpassing the compared ones by 3.84%–12.50%. For the size evaluation, the achieved mean errors of the topside area size of oil/gas platforms and diameter length measurement of wind turbines both are 1 pixel in Sentinel-2 images, providing an effective technique for the identification and estimation of offshore infrastructure.

Citation

MA, P., MACDONALD, M., ROUSE, S. and REN, J. 2024. Automatic geolocation and measuring of offshore energy infrastructure with multimodal satellite data. IEEE journal of oceanic engineering [online], 49(1), pages 66-79. Available from: https://doi.org/10.1109/joe.2023.3319741

• Journal Article Type: Article
• Acceptance Date: Sep 18, 2023
• Online Publication Date: Nov 17, 2023
• Publication Date: Jan 31, 2024
• Deposit Date: Dec 5, 2023
• Publicly Available Date: Dec 5, 2023
• Journal: IEEE journal of oceanic engineering
• Print ISSN: 0364-9059
• Electronic ISSN: 1558-1691
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 1
• Pages: 66-79
• DOI: https://doi.org/10.1109/joe.2023.3319741
• Keywords: Multimodal satellite data; Offshore oil/gas platforms; Offshore wind turbines; Size assessment
• Public URL: https://rgu-repository.worktribe.com/output/2153232