Background

Advances in artificial intelligence, hardware accelerators, and data processing architectures, continue to reach the geospatial information sciences, with a transformative impact on many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn representational features from massive and complex multimodal data. In tandem, rapid innovations in sensing technologies support the collection of geospatial data in even higher resolution and throughput, supporting the observation, mapping, and analysis of different events/phenomena over the Earth’s surface with unprecedented detail. Combined, these developments are offering the potential for breakthroughs in geographic knowledge discovery, impacting decision-making in areas such as humanitarian mapping and the handling of natural disasters, intelligent transport systems, urban expansion analysis, health data analysis and epidemiology, biodiversity science, assessments related to climate and sustainability, and the general monitoring of the Earth’s surface.

With a continued combination of artificial intelligence, spatiotemporal data computing, and geographic research, we invite you to join us at GeoAI 25′, which will be held alongside SIGSPATIAL 2025, in Minneapolis, MN, USA. Building on a successful series of workshops, ACM SIGSPATIAL GeoAI workshop continues to serve as a forum for advancing research and fostering collaboration in this rapidly evolving field.

Format

Similar to previous editions, we plan for a one-day workshop, including two keynotes (morning and afternoon, respectively) and individual presentations. A paper competition will also be organized for the presented papers. Three submission types will be considered for the workshop:

• Full research paper: 8-10 pages
• Short research paper or industry demo paper: 4 pages
• Vision or statement paper: 2 pages

Full research papers should present mature research on a specific problem or topic in the context of GeoAI. We also welcome short research articles or industry demonstrations of existing or developing methods, toolkits, and best practices for AI applications in the geospatial domain. A vision for future directions, or an overview statement on gaps and challenges for developing AI technology and its applications in the geospatial domain, is also welcome. All submitted papers will be peer-reviewed to ensure the quality and clarity of the presented research.

List of Topics

• Geospatial domain-guided and spatially-aware ML and AI;
• Explainable geospatial artificial intelligence (XGeoAI);
• Novel deep learning architectures and algorithms for GeoAI;
• Foundation GeoAI models and downstream applications;
• Conversational GeoAI assistants and applications;
• GeoAI and geographic knowledge graphs;
• Uncertainty quantification methods for GeoAI;
• GeoAI for spatio-temporal forecasting;
• Natural language processing for GeoAI;
• GeoAI for modeling networks and flows;
• Self-supervised and unsupervised methods in GeoAI;
• Human in the loop GeoAI methods;
• Data integrity, privacy, and ethics in GeoAI;
• Multimodal learning for GeoAI;
• Geo-information retrieval and recommendation; 
• Applications:
  • Earth observation and sustainability;
  • Health and epidemiology;
  • Precision agriculture;
  • Location intelligence; 
  • Urban growth prediction and planning;
  • Disaster response and humanitarian applications;
  • Mobility and traffic data analytics;
  • Earth system science and weather forecasting;
  • Biodiversity science;
  • Cartography;
  • Archaeology and anthropology

Organizing Committees

• Sam Arundel, U.S. Geological Survey

• Song Gao, University of Wisconsin, Madison

• Dalton Lunga, Oak Ridge National Laboratory

• Gengchen Mai, University of Texas at Austin

• Bruno Martins, University of Lisbon, Portugal

• Shawn Newsam, University of California, Merced

• Di Zhu, University of Minnesota, Twin Cities

• Lexie Yang, Oak Ridge National Laboratory

Venue

The workshop will be held and co-located with ACM SIGSPATIAL 2025 in Minneapolis, MN, USA