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A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams

Duong Nguyen 1, 2 Rodolphe Vadaine 3 Guillaume Hajduch 3 René Garello 4, 1 Ronan Fablet 1, 2
1 Lab-STICC_IMTA_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 IMT Atlantique - SC - Département Signal et Communications
3 CLS - Collecte Localisation Satellites (Entreprise)
4 IMT Atlantique - ITI - Département lmage et Traitement Information
Abstract : 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.
Keywords : variational recurrent neural networks maritime surveillance AIS anomaly detection deep learning vessel type identification trajectory reconstruction transfer learning
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176
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Submitted on : Monday, October 15, 2018 - 3:59:52 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:47 PM

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CNRS | UNIV-BREST | INSTITUT-TELECOM | ENIB | LAB-STICC | IMT-ATLANTIQUE

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Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet. A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams. 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2018, Turin, Italy. ⟨10.1109/DSAA.2018.00044⟩. ⟨hal-01808176v4⟩

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