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

TL;DR

This paper introduces a semi-supervised, geometry-based method for detecting fishing activity from AIS vessel data, utilizing clustering and RNNs to achieve high accuracy in identifying fishing patterns.

Contribution

It presents a novel semi-supervised approach combining geometric feature extraction and RNN classification for fishing activity detection from AIS data.

Findings

Achieved approximately 87% F-score on unseen vessel trajectories.

Demonstrated effectiveness of unsupervised clustering for labeling trajectory geometry.

Provided a comprehensive benchmark of RNN architectures for this task.

Abstract

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a significant role in tracking vessel activity and mapping mobility patterns such as those found in fishing. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology we show how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel's moving pattern which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. In this context, we propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall $F$-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the trajectory in time and observing their inherent geometry.