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arXiv · 1806.03972

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

Duong Nguyen·Rodolphe Vadaine·Guillaume Hajduch·René Garello·Ronan Fablet
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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 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.

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Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet. 2018-08-07. A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams. https://doi.org/10.1109/dsaa.2018.00044

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