Automatic Detection of Dark Ship-to-Ship Transfers using Deep Learning and Satellite Imagery

• URL: http://arxiv.org/abs/2404.07607v1
• Date: Thu, 11 Apr 2024 09:50:05 GMT
• Title: Automatic Detection of Dark Ship-to-Ship Transfers using Deep Learning and Satellite Imagery
• Authors: Ollie Ballinger,
• Abstract summary: No studies identify ship-to-ship transfers in satellite imagery. I train a convolutional neural network to accurately detect 4 different types of cargo vessel. I apply this method to the Kerch Strait between Ukraine and Russia to identify over 400 dark transshipment events since 2022.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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