ArticleNational science review2020
Deep-learning-based information mining from ocean remote-sensing imagery.
Article in National science review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Global distribution and amplitudes of internal solitary waves revealed by SWOT.Science advances · 2026Article
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Submesoscale daily data from a non-hydrostatic OGCM at 1/90° resolution over Northern South China Sea in 2019.Scientific data · 2026Article
- 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 · 2025Article
- Deep learning-based fishing ground prediction with multiple environmental factors.Marine life science & technology · 2024Article
- Unlocking secrets of microbial ecotoxicology: recent achievements and future challenges.FEMS microbiology ecology · 2023Review
- Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.National science review · 2022Article
- Use of semantic segmentation for mappingPeerJ · 2022Article
- A Dual-Polarimetric SAR Ship Detection Dataset and a Memory-Augmented Autoencoder-Based Detection Method.Sensors (Basel, Switzerland) · 2021Article
- High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery.Sensors (Basel, Switzerland) · 2021Article
- Purely satellite data-driven deep learning forecast of complicated tropical instability waves.Science advances · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning-a powerful technology recently emerging in the machine-learning field-has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.