Cluster 1: Urban Perception & Human-Centered AI Keenon Lindsey, Texas State Univ.: “Seeing” Gentrification: A Deep Learning Approach to Visual Change Perception Yingrui Zhao, Univ. of Maryland: An LLM-Guided Approach for Analyzing Public Sentiment associated with Transportation POI Visit Patterns Michaelmary Chukwu, Univ. of Maryland: From Gravity Models to Semantic Reasoning: Leveraging LLMs for Visual Destination Characterization
Cluster 2: Environment, Hazards & Remote Sensing Sandra Le, George Mason Univ.: A Spatiotemporal Analysis of Vegetation and Water Changes in Libya Extreme Rainfall 2023 Using Remote Sensing Products and the Google Earth Engine (GEE) Xin Dong, Univ. of Maryland: Predicting the spatio-temporal spread of Plasmodium vivax malaria using a human-movement–informed GeoAI model Aleksander Berg, Univ. of Colorado Boulder: Using Foundation Model Embeddings to Map Colorado's Built Hazard Interface for Wildfire
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Cluster 3: GeoAI Methods & Modeling Jikun Liu, Texas A&M Univ.: A Wide-and-Deep-Based Time Sequence Model for Predicting Power Outages Caused by Extreme Winter Storms Victor Irekponor, Univ. of Maryland: Text-to-Visualization for Spatially Varying Coefficient Models: Encoding SVC Visualization Principles in Language-Driven Workflows Zhihao Wang, Univ. of Maryland: TreeFinder: AI Everywhere in Forest Monitoring — A National-Scale GeoAI Benchmark for Individual Tree Mortality
Cluster 4: Spatial Theory & Advanced Methods Mengyu Liao, Univ. of Maryland: Change of Support as a Reasoning Layer in LLM-Based GIS Workflows Jina Kim, Univ. of Minnesota: Spatial Heterogeneity-Aware Cross-Indicator Transfer for Prediction in Label-Sparse Regions
