  In a world of global trading, maritime safety, security and efficiency are
crucial issues. We propose a multi-task deep learning framework for vessel
monitoring using Automatic Identification System (AIS) data streams. We combine
recurrent neural networks with latent variable modeling and an embedding of AIS
messages to a new representation space to jointly address key issues to be
dealt with when considering AIS data streams: massive amount of streaming data,
noisy data and irregular timesampling. We demonstrate the relevance of the
proposed deep learning framework on real AIS datasets for a three-task setting,
namely trajectory reconstruction, anomaly detection and vessel type
identification.
