NEWS

• The GeoAI’26 call for papers and tentative deadlines are now available.
• GeoAI’26 will be held on November 3, 2026 in Riverside, CA, USA, co-located with SIGSPATIAL 2026.
• Submission link on EasyChair.

Background

Following the success of previous GeoAI workshops at ACM SIGSPATIAL, GeoAI’26 aims to continue bringing together geoscientists, computer scientists, engineers, entrepreneurs, and decision makers from academia, industry, and government to discuss the latest trends, successes, grand challenges, and opportunities in Geospatial Artificial Intelligence (GeoAI). Through the workshop, attendees will exchange the latest information on methods, tools, datasets, and workflows that can impact GeoAI.

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 and foundation models have brought forward an automated capability to learn representational features from massive and complex multimodal data about the Earth and human-environment interactions. In tandem, rapid innovations in sensing technologies support the collection of geospatial data in ever higher resolution and throughput, enabling observation, mapping, and analysis of events and phenomena over the Earth’s surface with unprecedented detail.

By combining artificial intelligence, spatiotemporal data computing, and GIScience research, GeoAI’26 strongly fits the general theme of ACM SIGSPATIAL. The workshop will be held alongside SIGSPATIAL 2026 in Riverside, CA, USA, and will continue to serve as a forum for advancing research and fostering collaboration in this rapidly evolving field.

Call for Papers

The workshop will provide an interactive forum to engage in discussions, shape research directions, and disseminate state-of-the-art solutions. Example topics include, but are not limited to, the following:

• 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;
• Agentic GeoAI frameworks;
• 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 spatial networks, spatial interactions, 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;
• GeoAI methods in remote sensing;
• Geo-information retrieval and recommendation;
• Applications:
  • Earth observation and sustainability;
  • Public health and epidemiology;
  • Precision agriculture;
  • Food systems;
  • Location intelligence;
  • Urban planning and analytics;
  • Disaster response and humanitarian applications;
  • Transportation, mobility, and traffic data analytics;
  • Earth system science and weather forecasting;
  • Biodiversity and conservation science;
  • Cartography;
  • Archaeology and anthropology;

Submission Link

Submission link: EasyChair

Deadlines

• Paper submission: 21st August, 2026
• Decisions to authors: 18th September, 2026
• Camera-ready versions: 4th October, 2026
• Workshop day: 3rd November, 2026

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 (do not include references), with an additional 2-page appendix allowed
• 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 under-development 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.

Program Committee

• TBA

Steering Committee

Yingjie Hu, University at Buffalo