Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams

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. In the world of a globalized economy, maritime surveillance is a vital demand. Besides, the real-time delivery of maritime situation maps is also necessary for a variety of activities: fishing activities control, smuggling detection, EEZ intrusion detection, transshipment detection, maritime pollution monitoring, etc. Over the last decades, the development of terrestrial networks and satellite constellations of Automatic Identification System (AIS) has opened a new era in maritime traffic surveillance. Every day, AIS provides on a global scale hundreds of millions of messages [1], which contain ships' identifiers, their Global Positioning System (GPS) coordinates, their speed, course, etc. The potential of this massive amount of data is clearly of interest if tools and models provide means to efficiently extract, detect and analyze relevant information from these data streams. However, current operational systems, which strongly rely on human experts, can only deal with a limited fraction of AIS data streams. Thus, the development of AIbased systems is a critical challenge.