DSI | GeoAI: Past, Present, and Future

The Data Science Institute (DSI) hosted a seminar by Shawn Newsam from UC Merced on February 9, 2024. Read more about the DSI seminar series at data-science.l....
This talk focuses on GeoAI, which is the application of artificial intelligence (AI) to geographic data. First, Dr. Newsam will briefly describe some of his work in this area over the last 25 years which has been driven largely by two themes. One theme is that spatial data is special in that space (and time) provides a rich context in which to analyze it. The challenge is how to incorporate spatial context into AI methods when adapting or developing them for geographic data-that is, to make them spatially explicit. A
second theme is that location is a powerful key (in the database sense) that allows us to associate large amounts of different kinds of data. This can be especially useful, for example, for generating large collections of weakly labelled data when training machine learning models. In the second part of his talk, Dr. Newsam will discuss near-term opportunities in GeoAI related to foundation models particularly for multi-modal data. Finally, he will point out some anticipated challenges in GeoAI as generative models like OpenAI’s generative pre-trained transformer (GPT) become pervasive.
Dr. Shawn Newsam is a Professor of Computer Science and Engineering and Founding Faculty at the University of California, Merced. He has degrees from UC Berkeley, UC Davis, and UC Santa Barbara, and did a postdoc in the Sapphire Scientific Data Mining group in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory from 2003 to 2005. He is the recipient of a U.S. Department of Energy Early Career Scientist and Engineer Award, a U.S. National Science Foundation Faculty Early Career Development (CAREER) Award, and a U.S. Office of Science and Technology Policy Presidential Early Career Award for Scientists and Engineers (PECASE). He has held leadership positions in SIGSPATIAL, the ACM special interest group on the acquisition, management, and processing of
spatially-related information, including serving as the general and program chair of its flagship conference and as the chair of the SIG. His research interests include computer vision and machine learning particularly applied to geographic data.
LLNL-VIDEO-860860