Work

A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

Year: 2018

Type: conference-paper

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.

Source: IEEE International Conference on Data Science and Advanced Analytics (DSAA)

DOI: 10.1109/dsaa.2018.00044

Authors: Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, Rene Garello, Ronan Fablet

Institutions: Université de Bretagne Occidentale, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, IMT Atlantique, Collecte Localisation Satellites (France)

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: big data infraStructurE and analytics for AIS & Sentinel SAtellite Data for the surveillance of the MaritimE Traffic