Outlier detection in maritime environments using AIS data and deep recurrent architectures

TL;DR

This paper presents a deep recurrent neural network approach for maritime outlier detection using AIS data, improving the identification of anomalous ship movements for enhanced maritime surveillance.

Contribution

It introduces a bidirectional GRU-based encoder-decoder model for encoding and reconstructing ship trajectories, demonstrating superior performance in outlier detection tasks.

Findings

Bidirectional GRU models outperform traditional methods.

Deep learning effectively captures temporal maritime data dynamics.

Proposed approach improves outlier detection accuracy.

Abstract

A methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data, is presented in this paper. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding and reconstructing the observed ships' motion patterns. Our approach is based on a thresholding mechanism, over the calculated errors between observed and reconstructed motion patterns of maritime vessels. Specifically, a deep-learning framework, i.e. an encoder-decoder architecture, is trained using the observed motion patterns, enabling the models to learn and predict the expected trajectory, which will be compared to the effective ones. Our models, particularly the bidirectional GRU with recurrent dropouts, showcased superior performance in capturing the temporal dynamics of maritime data, illustrating the potential of deep learning to enhance maritime surveillance capabilities. Our work lays a solid foundation for future research in this domain, highlighting a path toward improved maritime safety through the innovative application of technology.