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Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness

Automatic vessel behaviour analysis is a key factor for maritime surveillance and relies on an efficient representation of knowledge about vessels activity. Emerging technologies such as space-based AIS provides a new dimension of service and creates a need for new methods able to learn a maritime scene model at an oceanic scale. In this paper, we propose such a framework: a probabilistic normalcy model of vessel dynamics is learned using unsupervised techniques applied on historical S-AIS data and used for anomaly detection and prediction tasks, thus providing functionalities for high-level situational awareness (level 2 and 3 of the JDL).