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

arXiv: https://arxiv.org/abs/2207.05514 · HTML: https://mlsanity.com/paper/2207.05514/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 59 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection](https://mlsanity.com/paper/1912.00682/) (2019)
- [A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams](https://mlsanity.com/paper/1806.03972/) (2018)
- [Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning](https://mlsanity.com/paper/2004.03722/) (2020)
- [Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation](https://mlsanity.com/paper/2003.10248/) (2020)

## Built on by (most cited first, 6 of 6)

- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning](https://mlsanity.com/paper/2401.05418/) (2023)
- [Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach](https://mlsanity.com/paper/2506.20359/) (2025)
- [A Systematic Approach for Selecting Trajectories for Data Augmentation](https://mlsanity.com/paper/2606.10938/) (2026)
- [MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories](https://mlsanity.com/paper/2606.17978/) (2026)

## Abstract (arXiv)

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.