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

TL;DR

This paper introduces a multi-task deep learning framework that effectively processes AIS data streams for maritime vessel monitoring, addressing challenges like data volume, noise, and irregular sampling to improve safety and security.

Contribution

It presents a novel multi-task deep learning architecture combining RNNs, latent variables, and message embeddings for comprehensive vessel analysis from AIS data.

Findings

Effective trajectory reconstruction demonstrated on real AIS data

Successful anomaly detection in vessel movements

Accurate vessel type identification achieved

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.