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.
Bio: Yiqun Xie is an Assistant Professor in the Center for Geospatial Information Science, Dept. of Geographical Sciences, and an Affiliate Faculty at the AI Interdisciplinary Institute at Maryland (AIM), at the University of Maryland. He received his PhD in Computer Science at the University of Minnesota, and his research addresses fundamental challenges facing AI for spatio-temporal data and scientific problems. His work focuses on advancing use-inspired AI models with explicit geo-awareness and knowledge-guidance to improve model generalizability for large-scale applications and accelerate scientific discoveries. He is leading multiple interdisciplinary AI projects funded by NSF, NASA, and Google, with research published in both top AI and domain science venues. His work has been recognized by multiple best paper awards from IEEE ICDM 2021, ACM SIGSPATIAL 2025 and 2019, SIAM Data Mining 2023, SSTD 2019, and was highlighted by the Great Innovative Ideas program by CCC at CRA. He delivered invited panel talks on GeoAI for committee meetings of the National Academies on Science, Engineering, and Medicine (NASEM) and the National Geospatial Advisory Committee (NGAC).
Building a successful GIS career goes beyond technical skills—it also requires strong professional connections, continuous learning, and engagement with the broader geospatial community. This workshop is designed for students, recent graduates, emerging GIS professionals, and seasoned professionals who want to strengthen their professional network, expand their skillset, and discover new opportunities for career growth within the GIS industry. Attendees will hear directly from these organizations:
Geospatial Professional Network (GPN)
Esri Young Professionals Network (YPN)
American Society for Photogrammetry and Remote Sensing (ASPRS)
Women in GIS (WiGIS)
GIS Certification Institute (GISCI)
Join this workshop as they share how their organizations support professional development, networking, leadership opportunities, mentoring, certifications, and career advancement within the geospatial field. The workshop will end with an engaging networking activity designed to help foster meaningful professional connections and provide practical strategies for building their own GIS professional ecosystem.
Sergii Skakun, Univ. of Maryland: ON THE INFERENCE FROM SATELLITE-BASED CLASSIFICATION MAPS FOR AREA ESTIMATION Luyu Liu, Auburn Univ.: Assessing the Potential and Bias of Generative Artificial Intelligence in Inferring Pedestrian Safety Perception Jielu Zhang, Harvard Univ.: SpatialCausal: a spatially-aware causal inference deep learning model for out-of-hospital cardiac arrest survival prediction Mehak Sachdeva, Florida State Univ.: Simulating the Precursor: Generative AI and Counterfactual Scenario Modeling of Partisan Mobility and Spatial Segregation (canceled)
This panel session aims to engage both panelists and the audience in a critical dialogue about the challenges, opportunities, and paths forward regarding the relationship between humans and GIS/GeoAI. We seek to explore how to develop a human-centered vision of GIScience and GeoAI that is both socially responsible and cognitively informed.
Moderator: Shih-Lung Shaw, University of Tennessee, Knoxville
Panelists: Kathleen Stewart, University of Maryland Xuebin Wei, James Madison University John Wilson, University of Southern California May Yuan, University of Texas at Dallas Zhe Zhang, Texas A&M University Bo Zhao, University of Washington
Dr. Shih-Lung Shaw is Chancellor’s Professor and Alvin and Sally Beaman Professor in the Department of Geography and Sustainability at the University of Tennessee, Knoxville. His research interests cover GIS for transportation, space-time GIS, time geography, transportation geography... Read More →
Lightning Talks Xuebin Wei, James Madison Univ.: Building an AI Teaching Stack for GeoAI Education Peter Kedron, UC Santa Barbara: Spatial Reasoning and identification in Causal Inference Lei Song, Rutgers Univ.: Explainable AI Reveals How Precipitation Modulates Thermal Effects Across Vertebrates Elizabeth Newnam, Temple Univ.: Effects of NDVI Exposure on Cannabis Use Disorder Treatment
Full Research Presentations Somayeh Dodge, UC Santa Barbara: What Mobile Location Data Can Tell Us About Nature Exposure? Isaac Rand, Federal Reserve Bank of Philadelphia: Automated Digitization of the Censuses of Housing Block Statistics, 1940-1970
The Maryland Robotics Center (3119 IDEA) is an interdisciplinary research center, part of the A. James Clark School of Engineering. The mission of the center is to advance robotic systems and applications of robotics through research and educational programs. Come join us to see some of the interesting robotics projects that are underway (cognitive robots, human-robot interaction, surface rovers and more). Please note that we plan to walk to the Maryland Robotics Center as it is just a short walk (7 mins) from ESJ.
Wednesday June 17, 2026 3:30pm - 5:00pm EDT Offsite
Ju He, Florida State Univ.: Estimating the Continuous Causal Effect of Spatial Distance on Outcomes Chen Zhang, Univ. of Connecticut: CASTED: Calibrated Anomaly-based Spatial-Temporal Event Detection Framework for Anomaly Detection in Resilience Curves Farnoosh Roozkhosh, Univ. of Georgia: Context-Augmented GeoAI for Public EV Charging: Predicting When and Where Charging Occurs Tongwei Xu, Univ. of Washington: The Geography of “AI Slop”: An Embedding-Based Spatial Analysis of AI-Generated Music Tang Sui, Univ. of Wisconsin–Madison: FireST-GraphNet: Wildfire Progression Prediction via a Temporal Graph Neural Network with Sequence Learning
