ArticleSensors (Basel, Switzerland)2022
A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion.Sensors (Basel, Switzerland) · 2024Article
- Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models.Scientific reports · 2024Article
- Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City.Sensors (Basel, Switzerland) · 2023Article
- Robust Learning with Noisy Ship Trajectories by Adaptive Noise Rate Estimation.Sensors (Basel, Switzerland) · 2023Article
- AIS Trajectories Simplification Algorithm Considering Topographic Information.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown 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 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. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall
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Registered trials
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