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

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/conferences/?conf=deepspatial22
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.