Abstract

The establishment of the Automatic Identification System (AIS) was revolutionary for Maritime Situational Awareness, as it allowed for the tracking of vessels carrying an AIS transponder, which is mandatory for, and not limited to, the majority of the commercial fleet. Despite the benefits of the widespread use of AIS for navigational safety and global maritime security, one cannot depend only on AIS sources in order to obtain the complete maritime situational awareness picture. In this paper we describe a multistage data-centric workflow that integrates satellite optical imagery and AIS data for automatic vessel detection that builds on (i) image processing techniques and (ii) Convolutional Neural networks. The experimental evaluation of our approach shows that our framework achieves an accuracy greater than 95%.