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Automatic Identification System (AIS) messages are useful for tracking vesselactivity across oceans worldwide using radio links and satellite transceivers.Such data plays a significant role in tracking vessel activity and mappingmobility patterns such as those found in fishing. Accordingly, this paperproposes a geometric-driven semi-supervised approach for fishing activitydetection from AIS data. Through the proposed methodology we show how toexplore the information included in the messages to extract features describingthe geometry of the vessel route. To this end, we leverage the unsupervisednature of cluster analysis to label the trajectory geometry highlighting thechanges in the vessel's moving pattern which tends to indicate fishingactivity. The labels obtained by the proposed unsupervised approach are used todetect fishing activities, which we approach as a time-series classificationtask. In this context, we propose a solution using recurrent neural networks onAIS data streams with roughly 87% of the overall $F$-score on the wholetrajectories of 50 different unseen fishing vessels. Such results areaccompanied by a broad benchmark study assessing the performance of differentRecurrent Neural Network (RNN) architectures. In conclusion, this workcontributes by proposing a thorough process that includes data preparation,labeling, data modeling, and model validation. Therefore, we present a novelsolution for mobility pattern detection that relies upon unfolding thetrajectory in time and observing their inherent geometry.

A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels