Review of intelligent maritime transportation systems facilitated by deep learning: A survey on safe navigation

Intelligent maritime transportation systems (IMTSs) have become increasingly critical for enhancing navigational safety, improving operational efficiency, and supporting autonomous decision-making in maritime domains. With the growing volume and complexity of maritime operations, IMTSs have evolved rapidly through the integration of emerging technologies such as the internet of things (IoT), satellite communication, and artificial intelligence (AI). Among these, deep learning (DL) has shown particular promise, offering powerful capabilities to extract complex patterns from large-scale maritime data and enabling advancements in applications such as ship detection, trajectory prediction, collision avoidance, and traffic flow modeling. Despite these developments, a comprehensive review that critically assesses the strengths and weaknesses of these models, especially the DL-based models used in IMTSs, is lacking. As such, this study contributes to bridging this gap with a quantitative review of the technological evolution of IMTS and a systematic analysis of DL-based research within IMTSs, covering key domains such as risk assessment, autonomous navigation, situation awareness, and intelligent decision-making. Furthermore, this study highlights key challenges in recent research and identifies future research directions. This study not only provides a holistic understanding of how DL has transformed maritime intelligence but also offers practical insights for developing safe and more efficient IMTSs.