Artificial Intelligence and Machine Learning Methods in Ocean Engineering

Abstract submission deadline

31 May 2027

Manuscript submission deadline

31 July 2027

Viewed by

487

Topic Information

Dear Colleagues,

Research on artificial intelligence (AI) and machine learning (ML) has been pursued in many areas from intelligent data analysis to industrial automation, with increasing applications in key ocean engineering-related fields—especially those covered in this Topic, including the control and environmental perception of underwater vehicles, as well as AI-/ML-enabled autonomous path planning and cruising. These core research directions rely on air–sea integrated perception and observation (a synergy of space–air and underwater sensing technologies) and a suite of key supporting technologies, such as underwater vision, underwater multi-modal perception, underwater high-precision positioning, underwater 3D reconstruction, and marine environmental information analysis via satellite and low-altitude remote sensing, as well as knowledge graphs and big data that provide macro-level information support for intelligent algorithms. This integrated perception system fuses air–space and underwater multi-source data to form a comprehensive, full-dimensional observation network, providing all-round environmental information support for underwater vehicle operations. It is clear that AI/ML technologies can effectively address the long-standing challenges of ocean engineering, such as harsh operating environments, complex marine dynamics, and massive heterogeneous data. This Topic focuses on these key technologies and research directions, providing opportunities for exchanging the theories and techniques of AI and ML that advance ocean engineering, fostering interdisciplinary collaboration and pushing the frontier of smart ocean engineering.

There are many scenarios where it is difficult to study and intervene in real-world marine engineering due to various physical limitations and technical constraints, particularly in the control, environmental perception, path planning and cruising of underwater vehicles. AI and ML methods offer powerful solutions to bridge this gap, while air–sea integrated perception and observation further breaks through the bottleneck of single-domain perception, enabling efficient data processing, accurate prediction, and intelligent decision-making in marine engineering. For example, with advanced AI/ML algorithms, researchers can leverage underwater vision and multi-modal perception (the underwater segment of air–sea integrated perception) to enhance the environmental awareness of underwater vehicles, rely on underwater high-precision positioning and 3D reconstruction to ensure navigation accuracy, and use AI-augmented satellite and low-altitude remote sensing (the air–space segment) to analyze marine environmental information—such as the analysis and modeling of ocean waves or seabed terrains—that supports autonomous path planning and cruising. Meanwhile, knowledge graphs and big data provide macro-level information analysis, laying a solid foundation for the operation of intelligent algorithms. To promote the in-depth integration of AI/ML with underwater vehicle technologies, as well as the synergy between remote sensing, knowledge graphs, big data, underwater systems and air–sea integrated perception and observation technologies, this Topic aims at consolidating views on recent trends and major challenges, presenting a platform to disseminate state-of-the-art research and exchange new thoughts that further advance the progress of smart ocean engineering.

Dr. Shu Zhang
Dr. Hongjie Ma
Prof. Dr. Muwei Jian
Dr. Haibin Cai
Dr. Xuewu Dai
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Coasts coasts	-	2.5	2021	27.7 Days	CHF 1200	Submit
Journal of Marine Science and Engineering jmse	3.2	5.6	2013	15 Days	CHF 2600	Submit
Oceans oceans	2.5	2.6	2020	27.2 Days	CHF 1600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Water water	3.5	6.7	2009	17.7 Days	CHF 2600	Submit

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