  The constant growth of maritime traffic leads to the need of automatic
anomaly detection, which has been attracting great research attention.
Information provided by AIS (Automatic Identification System) data, together
with recent outstanding progresses of deep learning, make vessel monitoring
using neural networks (NNs) a very promising approach. This paper analyses a
novel neural network we have recently introduced -- GeoTrackNet -- regarding
operational contexts. Especially, we aim to evaluate (i) the relevance of the
abnormal behaviours detected by GeoTrackNet with respect to expert
interpretations, (ii) the extent to which GeoTrackNet may process AIS data
streams in real time. We report experiments showing the high potential to meet
operational levels of the model.
