# Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning

arXiv: https://arxiv.org/abs/2004.03722 · HTML: https://mlsanity.com/paper/2004.03722/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

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

- [A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels](https://mlsanity.com/paper/2207.05514/) (2022)
- [Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning](https://mlsanity.com/paper/2202.13867/) (2022)
- [Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation](https://mlsanity.com/paper/2403.08838/) (2024)
- [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)
- [Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies](https://mlsanity.com/paper/2606.29721/) (2026)

## Abstract (arXiv)

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enormous volume of vessel data continually being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.