Automatic Detection of Dark Ship-to-Ship Transfers using Deep Learning and Satellite Imagery
This addresses a gap in monitoring illicit shipping practices for maritime security and enforcement agencies, though it is an incremental application of existing methods to a new domain.
The paper tackled the problem of detecting illicit ship-to-ship transfers in satellite imagery, which had not been studied before, and applied a deep learning method to identify over 400 dark transshipment events in the Kerch Strait since 2022.
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.