Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization

• URL: http://arxiv.org/abs/2510.11449v1
• Date: Mon, 13 Oct 2025 14:19:58 GMT
• Title: Enhancing Maritime Domain Awareness on Inland Waterways: A YOLO-Based Fusion of Satellite and AIS for Vessel Characterization
• Authors: Geoffery Agorku, Sarah Hernandez, Hayley Hames, Cade Wagner,
• Abstract summary: Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities.<n>This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS)<n>You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Maritime Domain Awareness (MDA) for inland waterways remains challenged by cooperative system vulnerabilities. This paper presents a novel framework that fuses high-resolution satellite imagery with vessel trajectory data from the Automatic Identification System (AIS). This work addresses the limitations of AIS-based monitoring by leveraging non-cooperative satellite imagery and implementing a fusion approach that links visual detections with AIS data to identify dark vessels, validate cooperative traffic, and support advanced MDA. The You Only Look Once (YOLO) v11 object detection model is used to detect and characterize vessels and barges by vessel type, barge cover, operational status, barge count, and direction of travel. An annotated data set of 4,550 instances was developed from $5{,}973~\mathrm{mi}^2$ of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrated vessel classification (tugboat, crane barge, bulk carrier, cargo ship, and hopper barge) with an F1 score of 95.8\%; barge cover (covered or uncovered) detection yielded an F1 score of 91.6\%; operational status (staged or in motion) classification reached an F1 score of 99.4\%. Directionality (upstream, downstream) yielded 93.8\% accuracy. The barge count estimation resulted in a mean absolute error (MAE) of 2.4 barges. Spatial transferability analysis across geographically disjoint river segments showed accuracy was maintained as high as 98\%. These results underscore the viability of integrating non-cooperative satellite sensing with AIS fusion. This approach enables near-real-time fleet inventories, supports anomaly detection, and generates high-quality data for inland waterway surveillance. Future work will expand annotated datasets, incorporate temporal tracking, and explore multi-modal deep learning to further enhance operational scalability.

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