The 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery


  • (6/19/2023) Launched workshop webpage.
  • (8/24/2023)Submission date is updated. The new due date for submissions is 9/15/2023.
  • (8/28/2023) Submit your paper to HERE.
  • (11/2/2023) Workshop program is available!
  • (11/9/2023) Keynote topics have been updated: GeoAI for Good by Professor Maria Antonia Brovelli and The Remote Sensing of Floods by Dr. Ronny Hänsch


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.

With a continued combination of artificial intelligence, spatiotemporal data computing, and geographic research, we invite you to join us at GeoAI’23, which will be held alongside SIGSPATIAL 2023, in Hamburg, Germany.


Example topics include but are not limited to:

  • 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

Workshop Chairs

shawn newsam

Shawn Newsam

University of California, Merced
Lexie Yang

Lexie Yang

Oak Ridge National Laboratory

Gengchen Mai

University of Georgia

Bruno Martins

University of Lisbon
Dalton Lunga

Dalton Lunga

Oak Ridge National Laboratory
Song Gao

Song Gao

University of Wisconsin Madison

Submission Details

Paper submission:  1st September, 2023      15th September, 2023

Acceptance decision: 25th September, 2023   6th October, 2023

Camera ready version: 10th October, 2023    20th October, 2023

Workshop date: 13th November, 2023


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-page 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.

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at All submitted papers will be peer reviewed to ensure the quality and the clarity of the presented research work. Submissions will be single-blind — i.e., the names affiliations of the authors should be listed in the submitted version.

Program Committee 

Abhishek Potnis Oak Ridge National Laboratory
Bo Peng PAII Inc.
Booma Sowkarthiga Balasubramani University of Illinois at Chicago
Dengfeng Chai Zhejiang University
Elif Sertel Istanbul Technical University
Gulsen Taskin Kaya Istanbul Technical University
Hao Li Techinical Universtiy of Munich
Hongxu Ma Google X
Jacob Arndt Oak Ridge National Laboratory
Jinmeng Rao
Jordan Bowman Oak Ridge National Laboratory
Lei Zou Texas A&M University
Martin Werner Technical University of Munich
Nikhil Makkar Purdue University
Philipe Dias Oak Ridge National Laboratory
Sylvain Lobry Université de Paris
Weiming Huang Nanyang Technological University
Wenwen Li Arizona State University
Xiaojiang Li Temple University
Xueqing Deng ByteDance research
Yao-Yi Chiang University of Minnesota
Yi Zhu Boson AI
Yingjie Hu University at Buffalo
Yu Zhang University of Kentucky
Yuxin Tian University of California Merced