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Call For Participation
We invite submissions for a session focused on the integration of Geographic Information Science (GIS) and Geospatial Artificial Intelligence (GeoAI) with Large Language Models (LLMs) to advance sustainable solutions. This session welcomes research that leverages LLMs in spatial data science to tackle challenges in diverse fields, from environmental management to urban resilience. Researchers and practitioners are encouraged to present empirical studies, applied methods, or theoretical contributions. Relevant topics include, but are not limited to:
LLM-Enhanced Spatial Data Analysis
Geospatial Knowledge Discovery through LLM
Geospatial LLM Agents
Ethical and Privacy Considerations
Interdisciplinary Applications of GIS and (Multimodal) LLMs

This/these session(s) is part of the Symposium on Spatial AI & Data Science for Sustainability (https://i-guide.io/aag-2025-symposium-on-spatial-ai-data-science-for-sustainability/), chaired by Shaowen Wang, Peter Darch, Jianguo “Jack” Liu, Fangzheng Lyu, and Zhe Zhang. To present a paper in any of the Symposium sessions, please register and submit your abstract online, and email your presenter identification number (PIN), paper title, and abstract to Wei Hu at weih9@illinois.edu by November 30th, 2024). We look forward to your submissions and participation!

Description:

As the world faces unprecedented sustainability challenges, the integration of Geographic Information Science (GIS), Geospatial Artificial Intelligence (GeoAI), and Large Language Models (LLMs) offers new and powerful tools to address complex issues in diverse fields. This session explores how advancements in LLMs are transforming spatial data science by enhancing capabilities for data analysis, knowledge discovery, and decision support. From environmental monitoring to urban resilience, LLMs can streamline workflows, unlock hidden insights, and provide real-time analysis across geospatial contexts.
The growing role of GeoAI and LLMs in GIS brings both opportunities and challenges. While these technologies enable more dynamic, accessible, and scalable applications in public health, disaster response, urban planning, and environmental management, they also present concerns about data privacy, ethical usage, and algorithmic bias. This session aims to explore both the innovative applications and critical reflections on LLM-driven GeoAI.
Presenters are encouraged to share empirical studies, applied methods, and theoretical perspectives that showcase the potential of LLMs in geospatial applications and address the ethical implications of using AI in spatial analysis. The discussions in this session will highlight interdisciplinary collaborations and advancements that leverage GeoAI and LLMs to drive sustainable solutions, offering new insights into the evolving landscape of spatial AI.

Non-Presenting Participants