  Representing maritime traffic patterns and detecting anomalies from them are
key to vessel monitoring and maritime situational awareness. We propose a novel
approach -- referred to as GeoTrackNet -- for maritime anomaly detection from
AIS data streams. Our model exploits state-of-the-art neural network schemes to
learn a probabilistic representation of AIS tracks and a contrario detection to
detect abnormal events. The neural network provides a new means to capture
complex and heterogeneous patterns in vessels' behaviours, while the \textit{a
contrario} detector takes into account the fact that the learnt distribution
may be location-dependent. Experiments on a real AIS dataset comprising more
than 4.2 million AIS messages demonstrate the relevance of the proposed method
compared with state-of-the-art schemes.
