Hal Daume, Volpi-Cupal Family Endowed Professor in the Department of Computer Science and Director of the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM)
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 →
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).
As AI fundamentally reshapes the professional landscape, this plenary panel explores the necessary pedagogical shifts, curricular integration strategies, and policy changes needed to prepare the next generation of geospatial scholars for an AI-infused future.
Panelists: Devika Jain, Harvard CGA Dan Goldberg, Texas A&M Bo Zhao, University of Washington
