A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
arXiv:2605.30388v1 Announce Type: new Abstract: This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, time gaps, and turn angles. Although unsupervised learning algorithms such as Isolation Forest are widely used for detecting anomalous vessel movements, they often lack systematic and meaningful evaluation measures. To address this limitation, we propose a novel quality metric called Maritime Anomaly Detection Quality Index (MADQI).
The proliferation of maritime data combined with advances in unsupervised AI techniques makes robust anomaly detection crucial and feasible now.
This development improves evaluation of AI systems in critical infrastructure, leading to more reliable and deployable autonomous maritime security solutions.
A standardized metric allows for more effective comparison and deployment of unsupervised AI models for maritime anomaly detection, enhancing security and efficiency.
- · Maritime surveillance industry
- · Naval forces
- · AI/ML developers
- · Shipping companies
- · Illicit maritime actors
- · Inefficient manual monitoring systems
Improved detection of illegal activities and unsafe behaviors at sea due to better-evaluated AI.
Increased confidence in autonomous maritime systems and potentially faster adoption of AI-driven vessel management.
The establishment of similar standardized evaluation metrics across other critical infrastructure sectors for AI deployment.
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Read at arXiv cs.LG