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

Duong Nguyen; Rodolphe Vadaine; Guillaume Hajduch; René Garello; Ronan Fablet

doi:10.1109/DSAA.2018.00044

Conference papers

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

Sciences of the Universe [physics] / Ocean, Atmosphere

Computer Science [cs] / Artificial Intelligence [cs.AI]

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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 : Monday, October 11, 2021 - 2:23:50 PM

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

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