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Advanced Studies in Marine Data Analysis
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Advanced Studies in Marine Data Analysis

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 1055

Special Issue Editors

Dr. Huanhuan Li

E-Mail Website
Guest Editor
School of Engineering, Liverpool John Moores University, Liverpool, UK
Interests: maritime safety and security; intelligent transportation systems; green shipping; autonomous shipping; onshore/offshore clean energy
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Zou

E-Mail Website
Guest Editor
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: navigation safety assessment of ships under constrained conditions; uncertainty quantification analysis; applications of machine learning in ship hydrodynamics; motion prediction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sheng Xu

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
Interests: marine hydrodynamics; offshore renewable energy; mooring dynamics; AI-driven techniques for real-time predictions of mooring dynamics, motion responses of offshore structures, and wave elevations
Special Issues, Collections and Topics in MDPI journals
Dr. Zifei Xu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Liverpool, Liverpool L69 7ZX, UK
Interests: fault diagnostics, prognostics, and intelligent solutions; reliability and safety technologies for mechanical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of marine data, driven by advancements in sensing technologies, automated monitoring, and digitalisation, has created new opportunities and challenges in maritime research. This Special Issue focuses on innovative methodologies and applications of marine data analysis, leveraging advanced artificial intelligence (AI), machine learning, deep learning, and big data analytics to enhance maritime operations, safety, and environmental sustainability.

This Special Issue welcomes research contributions that address key challenges in marine data analysis, including ship trajectory prediction, maritime situational awareness, cybersecurity risk assessment, energy-efficient route optimisation, and autonomous shipping technologies. Studies applying machine learning, deep learning, Bayesian networks (BNs), graph-based modelling, and hybrid AI approaches to extract valuable insights from automatic identification system (AIS) data and/or radar data, satellite observations, wind turbine data, and sensor networks are particularly encouraged. Additionally, research on predictive analytics for maritime safety, emission modelling for sustainable shipping, and AI-driven decision support systems will be considered.

By gathering state-of-the-art research in marine data analysis, this Special Issue aims to advance data-driven decision-making in maritime transportation, improve risk mitigation strategies, and promote the integration of AI in intelligent marine systems. We invite original research and review articles that contribute to developing robust, interpretable, and trustworthy AI solutions for complex maritime environments.

Dr. Huanhuan Li
Dr. Lu Zou
Prof. Dr. Sheng Xu
Dr. Zifei Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • marine data analytics
  • autonomous shipping
  • big data in maritime transportation
  • maritime cybersecurity
  • maritime situational awareness
  • AI-driven decision support systems
  • intelligent marine systems
  • risk assessment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

23 pages, 2708 KiB  
Article
Strategizing Artificial Intelligence Transformation in Smart Ports: Lessons from Busan’s Resilient AI Governance Model
by Jeong-min Lee, Min-seop Sim, Yul-seong Kim, Ha-ram Lim and Chang-hee Lee
J. Mar. Sci. Eng. 2025, 13(7), 1276; https://doi.org/10.3390/jmse13071276 - 30 Jun 2025
Viewed by 1
Abstract
The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human [...] Read more.
The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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28 pages, 4445 KiB  
Article
Link Availability-Aware Routing Metric Design for Maritime Mobile Ad Hoc Network
by Shuaiheng Huai, Tianrui Liu, Yi Jiang, Yanpeng Dai, Feng Xue and Qing Hu
J. Mar. Sci. Eng. 2025, 13(6), 1184; https://doi.org/10.3390/jmse13061184 - 17 Jun 2025
Viewed by 448
Abstract
A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states [...] Read more.
A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states and poses the risk of data transmission interruption. In this paper, a mobility model for small unmanned surface vessels based on smooth Gaussian semi-Markovian and a trajectory prediction method for large vessels based on a bi-directional long short-term memory network are proposed to better simulate the nodes’ movement in the M-MANET. Then, a link available based routing metric is proposed for M-MANET scenarios, which incorporates factors of mobility model and vessel trajectory. Experiments demonstrate that compared with the benchmark methods, the proposed mobility model depicts the movement characteristics of vessels more accurately, the proposed trajectory prediction method achieves higher prediction accuracy and stability, the proposed routing metric scheme has a reduction of 14.59% in end-to-end delay, a 1.54% increase in packet delivery fraction, and a 4.43% increase in network throughput on average. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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27 pages, 24008 KiB  
Article
A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity
by Weihao Tao, Yasong Luo, Jijin Tong, Qingtao Xia and Jianjing Qu
J. Mar. Sci. Eng. 2025, 13(6), 1085; https://doi.org/10.3390/jmse13061085 - 29 May 2025
Viewed by 273
Abstract
With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation [...] Read more.
With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation with contrastive-learning-optimized multi-scale similarity matching. First, a cascaded image preprocessing method is developed, incorporating linear transformation, bilateral filtering, and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to mitigate noise and haze interference and enhance image quality with improved target edge clarity. Subsequently, the DeepLabV3+ network is employed for the precise segmentation of ship targets, generating binarized contour maps for subsequent heading analysis. Based on actual ship dimensional parameters, 3D models are constructed and multi-angle rendered to establish a heading template library. The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. Experimental results demonstrate that under noise-free, noise-interfered, and mist-occluded conditions, the proposed method achieves mean heading estimation errors of 0.41°, 0.65°, and 0.88°, respectively, significantly outperforming the single-scale SSIM and fixed-weight MS-SSIM approaches. This verification confirms the method’s effectiveness and robustness, offering a novel technical solution for ship heading estimation in maritime surveillance and intelligent navigation systems. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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