A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
This work addresses maritime safety and efficiency for surveillance applications, but it is incremental as it builds on existing deep learning methods for AIS data.
The authors tackled the problem of maritime surveillance by developing a multi-task deep learning framework using AIS data streams, achieving results for trajectory reconstruction, anomaly detection, and vessel type identification on real datasets.
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.