DOI : https://doi.org/10.62527/comien.3.1.55

Anomaly Detection in Maritime Ship Trajectory Using a Deep Learning Approach: A Comprehensive Survey, State-of-the-Art and Future Perspective

Damap Tobias Yunana (1), Christopher Ifeanyi Eke (2), Gangfa Ezekiel Magaji (3)
(1) Department of Computer Science, Federal University Lafia, Nasarawa State, Lafia, Nigeria
(2) Federal University Lafia, Nasarawa State, Lafia, Nigeria
(3) Federal University Lafia, Nasarawa State, Lafia, Nigeria
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Yunana, D. T., Eke , C. I., & Magaji , G. E. (2026). Anomaly Detection in Maritime Ship Trajectory Using a Deep Learning Approach: A Comprehensive Survey, State-of-the-Art and Future Perspective. International Journal on Computational Engineering, 3(1), 1–9. https://doi.org/10.62527/comien.3.1.55
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Prediction of ship trajectories using data from the Automatic Identification System (AIS) has garnered increased attention due to its potential in averting collision incidents and resolving navigational conflicts. Hence, there exists a pressing need to systematically review the literature on deep learning prediction techniques to elucidate their benefits in ensuring maritime safety across various scenarios. This task is particularly important in the realm of unmanned vessels coexisting with manned ships, shaping a novel hybrid maritime traffic paradigm in the upcoming era. The present study aims to undertake a thorough review of deep learning methodologies, encompassing Recurrent Neural Networks, Long Short-Term Memory, auto-encoders, and Hybrid methods. The outcomes elucidate the distinctive features of diverse prediction approaches, offering valuable insights for stakeholders to navigate the selection of the most suitable method tailored to specific circumstances. Furthermore, it helps identify research challenges in ship trajectory prediction and proposes corresponding remedies to guide future investigations. These approaches contribute to improving detection performance, reducing false alarms, and anticipating proper actions in response to anomalous behaviors, ultimately enhancing maritime situational awareness and operational efficiency in complex maritime environments. Anomaly detection in maritime plays a crucial part in enhancing the safety of marine traffic and security, leveraging advanced technologies and innovative approaches.

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