Towards submesoscale eddy detection in SDGSAT-1 data through deep learning-Reference-Cited by-同舟云学术

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Towards submesoscale eddy detection in SDGSAT-1 data through deep learning

Published:2024-06-27 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Li Ruijiao¹,Xia Linghui¹,Yang Jie¹²,Huang Baoxiang³,Chen Ge¹²

Affiliation:

1. Department of Marine Technology, Ocean University of China, Qingdao, People’s Republic of China

2. Laoshan Laboratory, Qingdao, People’s Republic of China

3. College of Computer Science and Technology, Qingdao University, Qingdao, People’s Republic of China

Funder

Laoshan Laboratory science and technology innovation projects

International Research Center of Big Data for Sustainable Development Goals

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2369625

Reference45 articles.

1. Detection and characterization of submesoscale eddies from optical images: a case study in the Argentine continental shelf

2. Mesoscale to Submesoscale Transition in the California Current System. Part I: Flow Structure, Eddy Flux, and Observational Tests

3. End-to-End Object Detection with Transformers

4. Geographical Variability of the First Baroclinic Rossby Radius of Deformation

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