Artificial Intelligence for Ocean Remote Sensing (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 298
Special Issue Editors
Interests: ocean remote sensing; coastal remote sensing; deep-ocean remote sensing; global climate change; AI oceanography
Special Issues, Collections and Topics in MDPI journals
Interests: physical oceanography; ocean remote sensing; AI oceanography; data science; bio-physical coupling
Special Issues, Collections and Topics in MDPI journals
Interests: oceanography; remote sensnig; GIS applications
Interests: physical oceanography; ocean remote sensing; climate change; air-sea interaction; ocean circulation; image processing; environmental monitoring; deep learning/big data/data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The use of Artificial Intelligence (AI) has the potential to revolutionize the way we collect, analyze, and interpret data from the vast and complex oceans. AI oceanography has demonstrated its capability in the handling of various oceanic problems, from monitoring marine ecosystems and the environment to predicting ocean currents and weather patterns. Concurrently, propelled by the continuous development of remote sensing techniques over recent decades, ocean observation has entered the big data era. An increasing number of ocean satellites equipped with broad sensors have been deployed to view oceans from large-scale and high-resolution perspectives.
The fusion of AI and remote sensing has unleased great potential in dealing with remote sensing retrieval, feature/pattern recognition, and reconstruction problems. The underlying rules of hidden correlation can be revealed from the collected data to advance our understanding of oceans and contribute to more effective protection and management efforts. By further combining these with other oceanic data, such as numerical models and re-analyses, the challenges faced by traditional oceanography can be effectively mitigated, and a new data-driven direction for ocean remote sensing can emerge as a new paradigm.
The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in AI applications in remote sensing of oceans. We welcome papers that are written from both theoretical and applicative perspectives, as well as contributions regarding new advanced AI/machine learning, deep learning, and data science techniques for the remote sensing research community.
Prof. Dr. Hua Su
Dr. Wenfang Lu
Prof. Dr. Young-Heon Jo
Prof. Dr. Xiao-Hai Yan
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
- ocean remote sensing
- artificial intelligence
- machine learning and deep learning
- big data mining
- data-driven models
- ocean processes
- ocean color and environment
- coastal environment and disasters
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Related Special Issue
- Artificial Intelligence for Ocean Remote Sensing in Remote Sensing (13 articles)
