Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI for atmosphere-ocean sciences: advancements, challenges and ways forward.

National science review·2026
Same author

Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.

National science review·2022
Same author

Purely satellite data-driven deep learning forecast of complicated tropical instability waves.

Science advances·2020
Same author

The subpleural pulmonary microvasculature in newborn yak (Bos grunniens).

Veterinary research communications·2008
Same author

Experimental confirmation of potential swept source optical coherence tomography performance limitations.

Applied optics·2008

Related Experiment Video

Updated: Oct 15, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.0K

Deep-learning-based information mining from ocean remote-sensing imagery.

Xiaofeng Li1, Bin Liu2, Gang Zheng3

  • 1Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.

National Science Review
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning offers powerful solutions for extracting valuable information from the vast ocean remote-sensing big data. This review highlights deep learning frameworks and their applications in ocean mapping, demonstrating their effectiveness.

Keywords:
artificial intelligencebig dataimage classificationocean remote sensing

More Related Videos

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

914
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

756

Related Experiment Videos

Last Updated: Oct 15, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.0K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

914
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

756

Area of Science:

  • Oceanography
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Ocean remote sensing generates massive datasets (petabytes) daily, posing challenges for traditional data mining.
  • The emergence of deep learning presents a powerful alternative to traditional algorithms for information extraction.

Purpose of the Study:

  • To systematically review deep learning frameworks for ocean remote-sensing image classification.
  • To present diverse applications of deep learning in ocean remote sensing.

Main Methods:

  • Review of two primary deep learning frameworks for image classification.
  • Case studies of eight applications including internal waves, oil spills, sea ice, and coral reefs.

Main Results:

  • Deep learning frameworks show significant superiority over traditional methods for ocean image analysis.
  • Demonstrated effectiveness across various oceanographic features and phenomena mapping.

Conclusions:

  • Deep learning is highly effective for mining information from ocean remote-sensing big data.
  • The reviewed frameworks can be adapted for a wide range of remote-sensing applications.