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

arXiv 2404.07607 ↗First posted 11 Apr 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 11 Apr 2024.

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3 of 23 references.

References · 3

  1. 2023Segment Anythingcited by 8,234
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2023Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learningcited by 5

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