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

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

Authors

Malcolm Macdonald

Sally Rouse



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. 2023. Automatic geolocation and measuring of offshore energy infrastructure with multimodal satellite data. IEEE journal of oceanic engineering [online], Early Access. 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
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
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

Files

MA 2023 Automatic geolocation and measuring (AAM) (4.2 Mb)
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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