Background

Emerging advances from artificial intelligence, hardware accelerators, and data processing architectures continue to reach the geospatial information sciences, with a transformative impact in many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn hierarchical representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies are supporting 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 potential for breakthroughs in geographic knowledge discovery, impacting decision making in areas such as humanitarian mapping, intelligent transport systems, urban expansion analysis, health data analysis and epidemiology, the study of climate change, handling natural disasters, and the general monitoring of the Earth’s surface.

Following the success of the previous GeoAI workshops at SIGSPATIAL, GeoAI’23 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 the emerging field of geospatial artificial intelligence. We aim to provide actionable intelligence and power new geographic scientific discoveries. Through the workshop, attendees will be able to exchange the latest information on methods, tools, datasets, and workflows that can impact GeoAI. With a continued combination of artificial intelligence, spatiotemporal data computing, and geographic research, we invite you submitting and joining us at GeoAI'23 Workshop, co-located with ACM SIGSPATIAL 2023 in Hamburg, Germany. 

Submission Guidelines

This is a one-day workshop, which includes two keynotes (one for the morning and one for the afternoon respectively) and individual presentations. A paper competition will also be organized for the presented papers. Three submission types will be included in this workshop:

• Full research paper: 8-10 pages with 2 pages of appendix
• 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 geospatial AI. 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 the development of AI technology and their applications in the geospatial domain are also welcome. All submitted papers will be peer reviewed to ensure the quality and the clarity of the presented research work.

List of Topics

• Geospatial domain-guided machine learning algorithms (GeoAI);
• Explainable geospatial artificial intelligence (XGeoAI);
• Novel deep learning architectures and algorithms for geospatial information;
• Large foundation models for geospatial information and tasks;
• Representation learning for geospatial information;
• Network data analytics and geographic knowledge graphs;
• Self-supervised and unsupervised methods in GeoAI;
• Human in the loop methods for enhancing GeoAI;
• Natural language interfaces for geospatial information;
• Data integrity, privacy and ethics in GeoAI;
• Data fusion and multimodal GeoAI methods;
• Geospatial recommendation methods; 
• 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

Committees

Organizing committee

• Shawn Newsam, University of California, Merced

• Lexie Yang, Oak Ridge National Laboratory

• Gengchen Mai, University of Georgia

• Bruno Martins, University of Lisbon, Portugal

• Dalton Lunga, Oak Ridge National Laboratory

Venue

The workshop will be held and co-located with ACM SIGSPATIAL 2023 in Hamburg, Germany.