Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams

van Duong Nguyen (1) , Rodolphe Vadaine (2) , Guillaume Hajduch (2) , René Garello (1) , Ronan Fablet (1)

1 Lab-STICC_IMTA_CID_TOMS
2 CLS - Collecte Localisation Satellites (Entreprise)

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 time-sampling. 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.

Domaines: Océan, Atmosphère; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

van Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet. Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams. 2018. ⟨hal-01808176v2⟩