DeepSpatial 2020 : 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Sytems
Call For Papers
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
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.
Geo-imagery and point cloud analysis (for remote sensing, Earth science, etc.)
Deep learning for mobility and traffic data analytics
Location-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.
Disaster management and respond systems using deep learning
GIS systems using deep learning (e.g., mapping, routing, or visualization)
Mobile computing systems using deep learning
In addition, we encourage submissions of spatiotemporal deep learning methods that address problems related to the COVID-19 pandemic.
Shashi Shekhar, University of Minnesota
Jieping Ye, University of Michigan & Didi Chuxing
Liang Zhao, George Mason University
Xun Zhou, University of Iowa
Feng Chen, University of Texas, Dallas
Paper submission instructions: 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.
Important Dates: (all due Midnight Pacific Time).
Paper submission deadline: May 20, 2020
Notification of decision: June 15, 2020.
Camera-ready due: June 20, 2020.
Liang Zhao (George Mason University, email@example.com , 4400 University Drive, Fairfax, VA 22030)
Xun Zhou (University of Iowa, firstname.lastname@example.org, S280 PBB, Iowa City, IA 52242)
Feng Chen (University of Texas at Dallas, email@example.com, ECSS 3.901 UTD, 800 W. Campbell Road, Richardson, TX 75080)