Welcome to DeepSpatial 2021

2nd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
Augest, 2021
KDD-organized Virtual Conference

Aims and Scope

The significant advancements in software and hardware technologies stimulated the prosperities of the domains in spatial computing and deep learning algorithms, respectively. Recent breakthroughs in the deep learning field have exhibited outstanding performance in handling data in space and time in specific domains such as image, audio, and video. Meanwhile, the development of sensing and data collection techniques in relevant domains have enabled and accumulated large scale of spatiotemporal data over the years, which in turn has led to unprecedented opportunities and prerequisites for the discovery of macro- and micro- spatiotemporal phenomena accurately and precisely. The complementary strengths and challenges between spatiotemporal data computing and deep learning in recent years suggest urgent needs to bring together the experts in these two domains in prestigious venues, which is still missing until now.

This workshop will provide a premium platform for both research and industry to exchange ideas on opportunities, challenges, and cutting-edge techniques of deep learning in spatiotemporal data, applications, and systems.

Topics of Interest: We encourage submissions of papers that fall into (but not limited to) the following three broad categories:

Novel Deep Learning Techniques for Spatial and Spatio-Temporal Data:
Spatial representation learning and deep neural networks for spatio-temporal data and geometric data
Physics-guided and interpretable deep learning for spatial-temporal data
Deep generative models for spatio-temporal data
Deep reinforcement learning for spatio-temporal decision making problems

Novel Applications of Deep Learning Techniques to Spatio-temporal Computing Problems. :
Remote sensing imagery and point cloud analysis in Earth science (e.g., hydrology, agriculture, ecology, natural disasters, etc.)
Deep learning for mobility and traffic data analytics
Location-based social network data analytics, geosocial media data mining, spatial event prediction and forecasting, geographic knowledge graphs
Learning for biological data with spatial structures (bio-molecule, brain networks, etc.)
Challenges, Opportunities, and Early Progress in Deep Learning for COVID-19

Novel Deep Learning Systems for Spatio-temporal Applications:
Real-time decision-making systems for traffic management, crime prediction, accident risk analysis, etc.
GIS systems using deep learning (e.g., mapping, routing, or Smart city)
Mobile computing systems using deep learning
GeoAI Cyberinfrastructure for Earth science applications
Interpretable deep learning systems for spatio-temporal temporal data

Workshop Co-Chairs

Liang Zhao

Assistant Professor
Emory University

Xun Zhou

Associate Professor
University of Iowa

Zhe Jiang

Assistant Professor
University of Alabama

Robert Stewart

Senior Scientist
Oak Ridge National Lab (ORNL)

Shashi Shekhar

McKnight Distinguished University Professor
University of Minnesota

Jieping Ye

Vice President and Chief Scientist at Beike
Professor, University of Michigan

Publicity Chair
Yuanqi Du, George Mason University

Program Committee
Arnold Boedihardjo, DigitalGlobe
Wei Wang, Microsoft Research
Ray Dos Santos, Army Corps of Engineers
Chao Zhang, Georgia Tech
Yanjie Fu, UCF
Xuchao Zhang, NEC Lab North America
Yanfang Ye, Case Western Reserve University
Yanhua Li, WPI
Jing Dai, Google
Yiqun Xie, University of Maryland
Junbo Zhang, JD Digital
Jie Bao, JD Digital
Song Gao, University of Wisconsin, Madison
Jingyuan Wang, Beihang University
Lexie Yang, ORNL
Borko Furht, Florida Atlantic University
Taghi Khoshgoftaar, Florida Atlantic University

Paper Submission

Important Dates: (all due Midnight Anywhere on Earth).
Paper Submission: May 20, 2021 May 27, 2021
Paper Review Due: June 5, 2021
Notification of Acceptance: June 10, 2021
Camera-ready Papers: June 17, 2021
Workshop Date: TBD

The workshop welcomes the two types of submissions

  • Full research papers – up to 9 pages (8 pages at most for the main body and the last page can only hold references)

  • Vision papers and short system papers - up to 5 pages (4 pages at most for the main body and the last page can only hold references)

All manuscripts should be submitted in a single PDF file including all content, figures, tables, and references, following the format of KDD conference papers. Paper submissions need to include author information (review not double blinded). The manuscript should follow the KDD template: https://www.acm.org/publications/proceedings-template.

Papers should be submitted at: https://easychair.org/conferences/?conf=deepspatial21
Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters or short talks during the workshop and published on the workshop website. Besides, a small number of accepted papers may be selected to be presented as contributed talks. As a tradition, accepted workshop papers are NOT included in the ACM Digital Library. The authors maintain the copyright of their papers.



Keynotes Speakers


Accepted Papers






Contact Information

Liang Zhao, lzhao9@gmu.edu, Tel: (703) 993 5910
Xun Zhou, xun-zhou@uiowa.edu, Tel: (319) 384-3335
Zhe Jiang, zjiang@cs.ua.edu, Tel: (205) 348-5243
Robert Stewart, stewartrn@ornl.gov, Tel: (865) 574-7646