GeoAI 2019

 

Call for papers

We are excited to announce a third series of GeoAI workshops at SIGSPATIAL 2019, in Chicago, IL. GeoAI2019 aims to continue bringing together geoscientists, computer scientists, engineers, entrepreneurs, and decision makers from academia, industry, and government to discuss the latest trends, successes, challenges, and opportunities in the field of deep learning for geographical data mining, to provide actionable intelligence and power new geographic scientific discoveries. Through the workshop, attendees will be able to exchange the latest information on techniques and workflows used in artificial intelligence for spatial research. With a combination of geo-computational methods and geographic research, we invite you to join us at GeoAI2019.

 

We are inviting paper submission for the following categories:

  • vision and position papers: 2 pages
  • on-going academic and industry papers: 4 pages
  • research papers and production ready papers: 8 -10 pages

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

  • GIScience with artificial intelligence for earth sciences and sustainability;
  • Artificial intelligence for public health and agricultural applications;
  • Novel deep neural network architectures and algorithms for geographic information analysis;
  • Artificial intelligence methods for object extraction (such as roads and buildings) from remote sensing images;
  • Deep learning for geographic information extraction from text (e.g. social media, web documents, and news);
  • Urban growth prediction and planning with machine learning methods;
  • Artificial intelligence methods for autonomous transportation and high-precision maps;
  • Unsupervised learning methods for large geographical scientific discoveries;
  • Deep learning for disaster response and humanitarian applications;
  • Human in the loop methods for enhancing deep learning  applications;
  • Distributed computing methods for large scale geocomputing;
  • Novel training methods for large scale machine learning with geographical data;
  • Fusion of geographic attributed datasets to improve model estimation

Paper Format And Submission Guidelines

Full research papers should present mature research on a specific problem or topic in the context of AI for geospatial problems. Short research articles or industry demonstrations of existing or developing scalable methods, toolkits, and best practices for AI applications in the geospatial domain are also invited. Vision or position papers noting future directions or an overview of grand challenges for AI technology in geospatial applications are also welcome. All submitted papers will be peer reviewed to ensure the quality, clarity and relevance of the solicited work.

Manuscripts should be formatted using the ACM camera-ready templates available at http://www.acm.org/publications/proceedings-template.

Submit paper

Accepted papers will be considered for “Best Paper Award.”

Important Dates

Paper submission: August 28, 2019

Acceptance decision: September 27, 2019

Camera ready version: October 4, 2019

Workshop date: November 5, 2019

Keynote Speakers (TBD)

 

Workshop Venue

The GeoAI'19 workshop will be co-located with the 27th ACM SIGSPATIAL Conference in Chicago, USA.  More details on the conference venue and registration process, please visit: https://sigspatial2019.sigspatial.org/

Organizers

Shawn Newsam
Associate Professor of Electrical Engineering & Computer Science and a Founding Faculty member at the University of California, Merced, USA

Song Gao
Assistant Professor in GIScience, at the University of Wisconsin, Madison, USA

Dalton Lunga
A Geospatial Image Analysis and Machine Learning Scientist at Oak Ridge National Laboratory, USA

Yingjie Hu
Assistant Professor of GIScience, University at Buffalo, New York, USA

Budhendra Bhaduri
Director, National Security Emerging Technologies Division at Oak Ridge National Laboratory, USA

Bruno Martins
Assistant Professor, University of Lisbon.

Xun Zhou
Assistant Professor for Business Analytics, Tippie College, University of Iowa

Liang Zhao
Assistant Professor for spatiotemporal data mining, George Mason University

Feng Chen
Associate Professor for event and pattern detection in network data, University at Albany, SUN

For more information

Please contact Song Gao or Shawn Newsam.