Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review

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Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review

Yang, Ying / Liu, Yang / Li, Guorong / Zhang, Zekun / Liu, Yanbin

Highlights • This paper presents a comprehensive review of machine learning applications in maritime research, focusing on Automatic Identification System data. • It explores how machine learning enhances data quality and aids in maritime decision-making. • The review discusses challenges in applying machine learning to Automatic Identification System data and proposes future research directions. • Emphasizes the need for advanced machine learning models and the creation of benchmark Automatic Identification System datasets for improved analysis.

Abstract Automatic Identification System (AIS) data holds immense research value in the maritime industry because of its massive scale and the ability to reveal the spatial–temporal variation patterns of vessels. Unfortunately, its potential has long been limited by traditional methodologies. The emergence of machine learning (ML) offers a promising avenue to unlock the full potential of AIS data. In recent years, there has been a growing interest among researchers in leveraging ML to analyze and utilize AIS data. This paper, therefore, provides a comprehensive review of ML applications using AIS data and offers valuable suggestions for future research, such as constructing benchmark AIS datasets, exploring more deep learning (DL) and deep reinforcement learning (DRL) applications on AIS-based studies, and developing large-scale ML models trained by AIS data.