Aims and Scope
Deep learning has exhibited outstanding performance in handling data in space and time in specific domains such as image, audio, and video. Meanwhile, the novel applications such as location-based social media, data-driven climate and Earth science, and ride-sharing 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. Further developments of spatial/spatiotemporal computing and deep learning call for the synergistic techniques and the collaborations between different communities, as evidenced by the recent momentum in both domains. On one hand, fast-increasing large-scale and complex-structured spatiotemporal data requires the investigation and extension toward more scalable and powerful models than traditional ones in domains such as computational geography and spatial statistics. One the other hand, deep learning techniques are evolving beyond regular grid-based (e.g., images), tree-based (e.g., texts), and sequence-based (e.g., audio) data to more generic or irregular data in space and time (e.g., in transportation, geomorphology, and protein folding), which calls for the expertise in the domains such as spatial statistics, geodesy, geometry, graphics, and geography. Consequently, the aforementioned 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 researchers from both academia and industry to exchange ideas on opportunities, challenges, and cutting-edge techniques of deep learning for spatiotemporal data, applications, and systems. Papers will be accepted under the topics including, 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
Interpretable deep learning for spatial-temporal dataDeep 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. :
Geo-imagery and point cloud analysis (for remote sensing, Earth science, etc.)
Deep learning for mobility and traffic data analyticsLocation-based social network data analytics, spatial event prediction and forecasting
Learning for biological data with spatial structures (bio-molecule, brain networks, etc.)
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
Interpretable deep learning systems for spatio-temporal temporal data
In addition, we encourage submissions of spatiotemporal deep learning methods that address problems related to the COVID-19 pandemic.
Vice President, Didi Chuxing
McKnight Distinguished University Professor
Arnold Boedihardjo, DigitalGlobe
Manzhu Yu, PSU
Wei Wang, Microsoft Research
Chao Zhang, Georgia Tech
Yanjie Fu, UCF
Xuchao Zhang, NEC Lab North America
Ray Dos Santos, Army Corps of Engineers
Yanhua Li, WPI
Lingfei Wu, IBM Research
Yinghui Wu, WSU
Zhe Jiang, University of Alabama
Kangkook Jee, UT Dallas
Jing Dai, Google
Yuanqi Du, George Mason University
Important Dates: (all due Midnight Pacific Time).
June 1, 2020 June 15, 2020 (Firm deadline)
Notification of Acceptance:
June 15, 2020 July 5, 2020
June 20, 2020 July 10, 2020
Workshop Date: August 24, 2020
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).
Papers should be submitted at: https://easychair.org/my/conference?conf=deepspatial2020
Concurrent submissions to other journals and conferences are acceptable. Accepted papers will be presented as posters during the workshop and posted on the website. Besides, a small number of accepted papers may be selected to be presented as contributed talks.
Liang Zhao, email@example.com, Tel: (703) 993 5910
Xun Zhou, firstname.lastname@example.org, Tel: (319) 384-3335
Feng Chen, email@example.com, Tel: (571) 265-7769