Natural Language Maps: Generative AI for Spatial Data Generation, Querying, and Visualization

Abstract:
Maps are powerful, but making sense of them has traditionally required specialized expertise in GIS software, complex query formulation, and significant manual effort. Advances in large language models (LLMs) and generative AI are beginning to change this dynamic, opening new ways of working with spatial data that are far more intuitive. Instead of relying on specialized tools, users can now describe the data they need or write complex geographic questions, and intelligent systems can translate those intentions into concrete results. This talk will highlight recent progress in three key directions: generating realistic spatial datasets from textual descriptions, answering complex questions that combine spatial reasoning with external knowledge, and automatically creating styles that make map visualizations easier to comprehend. Taken together, these advances illustrate a new paradigm where geospatial data can be explored and understood through a natural and accessible interface.

Bio:
Ahmed Eldawy is an Associate Professor of Computer Science at the University of California Riverside. His research interests lie in the broad area of databases with a focus on big data management and spatial data processing. Ahmed led the research and development in many open source projects for big spatial data exploration and visualization including UCR-Star, an interactive repository for geospatial data with nearly four terabytes of publicly available data. He is a recipient of the highly prestigious NSF CAREER award, the 10-year Influential Paper Award in ICDE 2025, and the Best Application Paper award in SIGSPATIAL 2025. His work is supported by the National Science Foundation (NSF) and the US Department of Agriculture (USDA).

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