Artificial Intelligence Oceanography

Prof. Xiaofeng Li, IEEE Fellow, graduated from Zhejiang University in 1985. Later, he received a master's and a Ph.D. in physical oceanography from the First Institute of Oceanography, China, and North Carolina State University (North Carolina State University), USA. From 1997 to 2019, Xiaofeng Li worked for the National Oceanic and Atmospheric Administration (NOAA), mainly engaged in satellite oceanography, artificial intelligence, and big data research. Xiaofeng Li is the associated editor of IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, and the Executive Editor-in-Chief of the Journal of Remote Sensing. He is also an editorial board member of the International Journal of Digital Earth, Big Earth Data, and the Journal of Limnology and Oceanography. In addition, he has lectured nearly 100 times in more than 50 Chinese and foreign universities and scientific research institutions.He is currently a scientist at theInstitute of Oceanology, Chinese Academy of Sciences, a Doctoral Student/Postdoctoral Supervisor, the Chief Scientist of artificial intelligence oceanography, and the Chief Engineer of the big data center.Dr. Li published a book with Springer before:Hurricane Monitoring With Spaceborne Synthetic Aperture Radar (Springer Natural Hazards) ISBN-13: 978-9811028922Fan Wang, Ph.D., Director of Institute of Oceanology, Chinese Academy of Sciences. From 1985 to 1995, he studied at Ocean University of China, majoring in Physical Oceanography, and obtained a Ph.D. degree; from 1995 to 1997, he did postdoctoral research in Institute of Oceanology, Chinese Academy of Sciences. Visiting Scholar at Texas A&M University in 2000 and National Oceanography Centre in UK in 2004. Dr. Wang started working at the Institute of Oceanology, Chinese Academy of Sciences since 1998. He is mainly working on ocean circulation dynamics, including the low-latitude western boundary currents and tropical circulation in the Pacific Ocean, and shelf circulations in the China Seas, etc. Since 1998, he has presided over 30 scientific research topics including projects of National Basic Research Program, major and key projects of the National Natural Science Foundation, and strategic pilot science and technology project of the Chinese Academy of Sciences; published more than 250 papers (including More than 150 SCI and EI papers) and 3 monographs. He was awarded as the National Outstanding Scientist and Engineer, won the Outstanding Award of Science & Technology of CAS, the First Prize of Science and Technology Award of Shandong Province, and the First Prize of Ocean Engineering Science and Technology Award. Dr. Wang serves as the Chairman of the International Northwest Pacific Ocean Circulation and Climate Experiment (NPOCE) science steering committee, the member of Climate and Ocean - Variability, Predictability, and Change Pacific Region Panel (CLIVAR/PRP), the member of the North Pacific Ocean Science Organization Physical Oceanography and Climate Committee (PICES/POC); Executive vice president of the Chinese Society for Oceanology and Limnology, the vice president of the Chinese Society for Oceanography, the vice chair of Chinese Committee for Marine Science Research (SCOR); the editor in chief of "Oceanology and Limnology"; Dean of College of Marine Science, University of Chinese Academy of Sciences.

• Theory and technology of artificial intelligence for oceanography.- Satellite data-driven internal wave forecast model based on machine learning techniques.- Detection and analysis of marine macroalgae based on artificial intelligence.- Tropical cyclone intensity estimation from geostationary satellite imagery.- Reconstructing marine environmental data based on deep learning.- Detecting oceanic processes from space-borne sar imagery using machine learning.- Deep convolutional neural networks-based coastal inundation mapping for un-defined least developed countries: taking madagascar and mozambique as examples.- Ai- based mesoscale eddy study.- Classifying sea ice types from sar images based on deep fully convolutional networks.- Detecting ships and extracting ship's size from SAR images based on deep learning.- Quality control of ocean temperature and salinity data based on machine learning technology.- automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks.- Automatic extraction of waterlines from large-scale tidal flats on SAR images and applications based on deep convolutional neural networks.- Forecast of tropical instability waves using deep learning.- Sea surface height prediction based on artificial intelligence.