Automatic Detection of Dark Ship-to-Ship Transfers using Deep Learning and Satellite Imagery (2404.07607v1)
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.
- “Identifying global patterns of transshipment behavior,” vol. 5.
- European Comission, “11th package of sanctions,” .
- “HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation,” vol. 8, pp. 120234–120254.
- “Ship detection based on deep learning using SAR imagery: a systematic literature review,” vol. 27, no. 1, pp. 63–84.
- “An automatic ship detection method adapting to different satellites SAR images with feature alignment and compensation loss,” vol. 60, pp. 1–17, Conference Name: IEEE Transactions on Geoscience and Remote Sensing.
- “A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection,” vol. 198, pp. 16–29.
- “Multi-scale ship detection from SAR and optical imagery via a more accurate YOLOv3,” vol. 14, pp. 6083–6101, Conference Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
- “A complete processing chain for ship detection using optical satellite imagery,” vol. 31, no. 22, pp. 5837–5854, Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/01431161.2010.512310.
- “Ship detection from optical satellite images with deep learning,” in 2019 9th International Conference on Recent Advances in Space Technologies (RAST), pp. 479–484.
- “Ship detection and classification from optical remote sensing images: A survey,” vol. 34, no. 3, pp. 145–163.
- G. Máttyus, “NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES,” vol. XL-1-W1, pp. 233–237, Conference Name: WG I/4, III/4, IC IV/VIII, VII/2<br/>ISPRS Hannover Workshop 2013 (Volume XL-1/W1) - 21 – 24 May 2013, Hannover, Germany Publisher: Copernicus GmbH.
- “Characterization of a bayesian ship detection method in optical satellite images,” vol. 7, no. 2, pp. 226–230, Conference Name: IEEE Geoscience and Remote Sensing Letters.
- “Ship detection from optical satellite images based on sea surface analysis,” vol. 11, no. 3, pp. 641–645, Conference Name: IEEE Geoscience and Remote Sensing Letters.
- “Inshore ship detection based on convolutional neural network in optical satellite images,” vol. 11, no. 11, pp. 4005–4015.
- “Global hot spots of transshipment of fish catch at sea,” vol. 4, no. 7, pp. eaat7159, Publisher: American Association for the Advancement of Science.
- “Analysis of fuzzy logic systems types 1 and 2 in identifying of IUU fishing and transshipment: a case study in indonesia’s vulnerable waters,” vol. 972, no. 1, pp. 012060, Publisher: IOP Publishing.
- “Transshipment activities in the north pacific fisheries commission convention area,” vol. 146, pp. 105299.
- “Identification of IUU transshipment activity using AIS data,” in 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–7.
- “Detecting intentional AIS shutdown in open sea maritime surveillance using self-supervised deep learning,” pp. 1–12, Conference Name: IEEE Transactions on Intelligent Transportation Systems.
- “Segment anything,” .
- “You only look once: Unified, real-time object detection,” pp. 779–788.
- British Petroleum, “Approximate conversion factors,” .
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