Prof. Xiaofeng Li of Chinese Academy of Sciences, Institute of Oceanography

Artificial intelligence (AI) is a major driving force behind the latest technological revolution and industrial transformation, particularly in its integration with big data and remote sensing, which has brought significant advancements to oceanographic research and monitoring. Traditional oceanographic modeling heavily relies on prior knowledge, whereas data-driven approaches now enable us to more efficiently and reliably transform multi-source satellite and in situ observational datasets into valuable insights. This transformation allows for a deeper exploration of oceanic phenomena and processes while enhancing the capability of remote sensing technologies for marine applications.

This talk will provide an in-depth overview of the latest AI applications in oceanography and remote sensing, including satellite-based information extraction for mesoscale eddies, internal waves, sea ice, ship detection, oil spills, and flooding caused by typhoons. Additionally, it will cover lightweight forecasting techniques for small-scale internal waves, mesoscale sea level variations, and large-scale equatorial instability waves using satellite remote sensing data and AI-driven models. These advancements provide powerful tools and methodologies for improving the interpretation, prediction, and real-time monitoring of oceanic and atmospheric phenomena through remote sensing technologies.

Biography:

Xiaofeng Li is an IEEE Fellow, Asia-Pacific Artificial Intelligence Association (AAIA) Fellow, and Fellow of the Electromagnetics Academy. He earned his Ph.D. in Physical Oceanography from North Carolina State University, USA.

Dr. Li has over 20 years of experience at the National Oceanic and Atmospheric Administration (NOAA), USA, specializing in satellite oceanography, big data, and artificial intelligence applications. He is currently a Professor and Chief Scientist for AI Oceanography at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS), where he also serves as the Chief Engineer of the Big Data Center.

Dr. Li is serving as: Associate Editor of IEEE Transactions on Geoscience and Remote Sensing (TGRS); Editor of JGR: Machine Learning and Computation, and Executive Editor of Science Partner Journal: Journal of Remote Sensing. He has authored and edited three books and published over 200 SCI-indexed papers.

Prof. Qing Xu of Ocean University of China, Faculty of Information Science and Engineering

We are currently in the era of big satellite data. However, satellite-based ocean observations still encounter several challenges. For example, the relatively low spatial resolution of certain satellite sensors limits our accurate understanding of the fine-scale characteristics of ocean variables, particularly in coastal regions. Furthermore, how to comprehensively utilize satellite data to capture three-dimensional structural features of the ocean remains a critical issue. In response to these challenges, this talk introduces our primary progress in reconstructing ocean data through synergistic integration of satellite observations and deep learning technologies. The main work includes super-resolution enhancement of microwave brightness temperature data, and reconstruction of three-dimensional underwater temperature and salinity fields based on satellite
observations of sea surface variables. These outcomes are anticipated to provide essential data and technical support for oceanographic research and its practical applications.

Biography:

Qing Xu earned her Ph.D. in Physical Oceanography from Ocean University of China in 2007. She is currently a Professor at the Faculty of Information Science and Engineering, Ocean University of China.

Dr. Xu served as a Postdoctoral Fellow at the Institute of Space and Earth Information Science, the Chinese University of Hong Kong from 2007 to 2009. She worked at the Department of Physical Oceanography, Hohai University, China, from 2009 to 2021, and has been a full-time Professor there since July 2016. In 2006 and 2015, she conducted one-year academic collaborations at the Department of Atmospheric and Oceanic Science, University of Maryland, and the Center for Coastal Physical Oceanography, Old Dominion University, USA, respectively.

Her research interests include satellite remote sensing and the application of artificial intelligence in oceanography. She has authored/co-authored nine books and published over 100 peer-reviewed papers.