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

TL;DR

This paper reviews how Transformer models are applied to AIS data for maritime monitoring, focusing on trajectory prediction and behavior detection, and analyzes datasets to support future research in the field.

Contribution

It provides a comprehensive overview of Transformer-based AIS data applications and organizes publicly available datasets with analysis, highlighting future research directions.

Findings

Transformer models effectively capture vessel movement patterns.

AIS datasets reveal operational characteristics of vessel types.

Organized datasets support further maritime monitoring research.

Abstract

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.