The future of Spatial Intelligence - The opportunity for Leadership In the not-too-distant future, every decision - investment, infrastructure, climate adaptation, real estate, farming, banking - will start with a live conversation with the Earth. In our field, we have surely moved from looking at the Earth to asking it questions building on the maturity of geospatial technology and computing, the explosion of earth observation data, and the proliferation of AI. In an era of 'AI Everywhere,' the path to true spatial intelligence requires more than just technological advancement; it demands a radical shift in how we collaborate.
In her keynote, Marge Cole draws on her global experience working with NASA, OGC and a multitude of startups and innovators over the years. Reflecting on the path towards spatial intelligence, its impact on innovation, research, and business opportunties - underscoring the pivotal need for academia and research to forge more cross-disciplinary, more collaboration, more agility, and more partnerships with industry upfront and throughout the research process, also examining the unique opportunities and challenges this rapid evolution presents for education and research.
Driven by a passion for innovation that empowers, I have spent over two decades at the intersection of aerospace, geospatial standards, and entrepreneurship. From my tenure at NASA, SGT and KBR to launching my own consulting firm and now LunateAI, I have a proven track record of championing... Read More →
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
Students Jayanta Biswas, UNC Charlotte: A Deep Learning Framework for Fusing Multi-Modal Environmental Data to Downscale Human Mobility for Precision Malaria Modeling in Zambia Arati Budhathoki, Clemson Univ.: Tracking Mountain Degradation for the United Nations (UN) Sustainable Development Goals (SDGs) Using the State of Colorado (USA) as an Example Sofiia Drozd, NTUU KPI: Agentic AI Framework for Automated Mapping of War-Damaged Agricultural Risk Zones and Satellite Data Retrieval Paul C. Dunn, Oregon State Univ.: Improving Multiscale Pattern–Process Analysis with a Eulerian-Lagrangian Flow Model and Uncertainty Aware Clustering Analysis Maxwell Gundling, Salisbury Univ.: From Silos to Spatial Data: An Enterprise GIS for Historical Research Fatemeh Janatabadi, George Mason Univ.: Artificial Intelligence Drives a New Feedback Loop Between Human Mobility and Urban Landscapes SiyuLu,Texas A&M Univ.: Deep Learning versus Traditional Interpolation for Elevation Reconstruction: Evaluating Performance Gains from Terrain-Based Auxiliary Variables Oliver Matus-Bond, Macalester College: Mapping the spatial relationship between invasive Melaleuca quinquenervia and fire occurrence in southeastern Madagascar Haley Mullen, Univ. of Maryland: LLM-based generation of geospatial synthetic data for predicting chronic disease Hossein Naderi, Texas A&M Univ.–Corpus Christi: Using Large Language Models to Quantify Urban Environments from Google Street View Zahra Salehi, Univ. of Connecticut: Spatial Intelligence for Agrivoltaic Land Suitability: A GIS-Based Multi-Criteria Decision Framework in Connecticut Rachel Simon, Salisbury Univ.: From Surface to Subsurface: Mapping Cemeteries in Dorchester County, Maryland Daryna Skakun, Urbana High School: Agentic AI for Environmental Impact Assessment of Construction Projects Using Satellite Data Ruichen Wang, Univ. of Maryland: Coincident Data Discovery Engine (CoDD): Enabling Global Cross-Platform Satellite Data Discovery Zhihao Wang, Univ. of Maryland: CarbonGlobe: A Global ML-Ready Benchmrk for Long-Term Carbon Forecasting Under Climate Change
Faculty & Other Wataru Morioka, Salisbury Univ.: Spatial Thinking–Centered GIS Curriculum: Problem Solving, Collaboration, and AI Era Pedagogy at Salisbury University
Scaling over Space: Advancing the Model and Data Foundations of GeoAI Advances in deep learning and foundation models are raising expectations for general-purpose learning and creating new opportunities to harness the geospatial data revolution for Earth monitoring and scientific discovery, with broad benefits for agriculture, energy, water, transportation, smart cities, and disaster response. At the same time, major challenges remain for large-scale geospatial applications, including spatial variability that substantially weakens model generalization, limited and localized training data, and high computational demands that constrain and slow scientific discovery. This talk will discuss both AI model and data foundations for scaling geospatial applications, including geo-aware learning, knowledge-guided learning, task-aligned pretraining, and new benchmark datasets.
