Ballinger, Ollie;
(2024)
Automatic Detection of Dark Ship-to-Ship Transfers Using Deep Learning and Satellite Imagery.
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
pp. 8943-8948.
10.1109/IGARSS53475.2024.10641983.
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Abstract
Despite extensive research into ship detection via remote sensing, no studies identify ship-to-ship transfers in satellite imagery. Given the importance of transshipment in illicit shipping practices, this is a significant gap. In what follows, I train a convolutional neural network to accurately detect 4 different types of cargo vessel and two different types of Ship-to-Ship transfer in PlanetScope satellite imagery. I then elaborate a pipeline for the automatic detection of suspected illicit ship-to-ship transfers by cross-referencing satellite detections with vessel borne GPS data. Finally, I apply this method to the Kerch Strait between Ukraine and Russia to identify over 400 dark transshipment events since 2022.
| Type: | Article |
|---|---|
| Title: | Automatic Detection of Dark Ship-to-Ship Transfers Using Deep Learning and Satellite Imagery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IGARSS53475.2024.10641983 |
| Publisher version: | https://doi.org/10.1109/igarss53475.2024.10641983 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201655 |
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