# AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review

arXiv: https://arxiv.org/abs/2505.07374 · HTML: https://mlsanity.com/paper/2505.07374/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning](https://mlsanity.com/paper/2310.15586/) (2023)
- [Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)](https://mlsanity.com/paper/2405.09596/) (2024)
- [Spatial and social situation-aware transformer-based trajectory prediction of autonomous systems](https://mlsanity.com/paper/2406.02767/) (2024)
- [Improved context-sensitive transformer model for inland vessel trajectory prediction](https://mlsanity.com/paper/2406.02771/) (2024)
- [From Digitalization to Data-Driven Decision Making in Container Terminals](https://mlsanity.com/paper/1904.13251/) (2019)
- [Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach](https://mlsanity.com/paper/2406.02344/) (2024)
- [Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models](https://mlsanity.com/paper/2406.02770/) (2024)

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

- [AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting](https://mlsanity.com/paper/2508.07668/) (2025)
- [MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)](https://mlsanity.com/paper/2507.20362/) (2025)
- [SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction](https://mlsanity.com/paper/2601.18537/) (2026)
- [Predictive Modeling of Maritime Radar Data Using Transformer Architecture](https://mlsanity.com/paper/2512.17098/) (2025)
- [AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks](https://mlsanity.com/paper/2606.06311/) (2026)
- [Protocol before progress: leakage-aware evaluation of AIS trajectory prediction](https://mlsanity.com/paper/2609.25827/) (2026)

## Abstract (arXiv)

With the increasing demands for safety, efficiency, and sustainability in global shipping, Automatic Identification System (AIS) data plays an increasingly important role in maritime monitoring. AIS data contains spatial-temporal variation patterns of vessels that hold significant research value in the marine domain. However, due to its massive scale, the full potential of AIS data has long remained untapped. With its powerful sequence modeling capabilities, particularly its ability to capture long-range dependencies and complex temporal dynamics, the Transformer model has emerged as an effective tool for processing AIS data. Therefore, this paper reviews the research on Transformer-based AIS data-driven maritime monitoring, providing a comprehensive overview of the current applications of Transformer models in the marine field. The focus is on Transformer-based trajectory prediction methods, behavior detection, and prediction techniques. Additionally, this paper collects and organizes publicly available AIS datasets from the reviewed papers, performing data filtering, cleaning, and statistical analysis. The statistical results reveal the operational characteristics of different vessel types, providing data support for further research on maritime monitoring tasks. Finally, we offer valuable suggestions for future research, identifying two promising research directions. Datasets are available at https://github.com/eyesofworld/Maritime-Monitoring.