Dr. Desheng Liu
Dr. Desheng Liu received the Ph.D. degree in environmental science from the University of California, Berkeley, CA, USA, in 2006. He is currently an Associate Professor with the Department of Geography and the Department of Statistics at the Ohio State University, Columbus, OH, USA. His research focuses on developing geospatial and statistical methodologies for modeling and monitoring environmental and ecological processes. His research has been funded by the U.S. National Science Foundation (NSF), the U.S. Department of Agriculture (USDA), and the National Aeronautics and Space Administration (NASA). Dr. Liu is an Associate Editor of Photogrammetric Engineering & Remote Sensing and serves on the editorial boards for four academic journals in remote sensing and environmental science. He has also served as a board member of The International Association of Chinese Professionals in Geographic Information Science (CPGIS). He was a NASA Earth System Science Fellow and a recipient of the Robert N. Colwell Fellowship from the American Society for Photogrammetry and Remote Sensing (ASPRS).
Title: Spatiotemporal Mining of Earth Observation Data: Challenges and Opportunities
Recent advances in remote sensing technologies have led to the acquisition of massive amounts of earth observation (EO) data from a wide range of platforms and sensors with various spatial resolutions and temporal frequencies. The rich spatial and temporal information in EO data presents an unprecedented opportunity to better understand our changing planet. However, the massiveness, complexity, and diversity of EO data also pose special challenges to automatically identify meaningful patterns and relationships from EO data for scientific discovery. While existing data mining techniques have shown great potential for EO data, there is still a strong need for developing more specialized EO data mining tools to keep up the explosive growth in EO data and address new challenges faced by the scientific community. This talk will first give an overview of existing spatiotemporal data mining techniques applied to EO data. It will then discuss emerging challenges and opportunities of developing effective spatiotemporal data mining techniques for geospatial information extraction and knowledge discovery.