Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean

TL;DR

This paper introduces GeoTrackNet, a neural network designed for real-time detection of abnormal vessel behaviors from AIS data, demonstrating promising results for maritime anomaly monitoring.

Contribution

The paper presents a novel neural network model, GeoTrackNet, tailored for operational real-time vessel anomaly detection using AIS data streams.

Findings

High potential for real-time anomaly detection

Strong correlation with expert interpretations

Effective processing of AIS data streams

Abstract

The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic Identification System) data, together with recent outstanding progresses of deep learning, make vessel monitoring using neural networks (NNs) a very promising approach. This paper analyses a novel neural network we have recently introduced -- GeoTrackNet -- regarding operational contexts. Especially, we aim to evaluate (i) the relevance of the abnormal behaviours detected by GeoTrackNet with respect to expert interpretations, (ii) the extent to which GeoTrackNet may process AIS data streams in real time. We report experiments showing the high potential to meet operational levels of the model.