RIASSUNTO
In this paper we demonstrate how deep learning can be applied to the field of sea surveillance by classifying ship types from their trajectories. Commercial ships using AIS continually report information such as their ship type, e.g. fishing or cargo ship. A problem with AIS information however is that it can easily be modified and therefore deliberately or accidentally incorrect. In an attempt to address this we use a 1100 hours long AIS data set to teach 16 different neural networks to classify ships using only motion trajectories and without relying on the reported ship type. We also test three baseline methods using a more conventional1-nearest neighbor approach. The evaluation showed that the best performing classifier was the one based on deep learning.