The significant advancements in software and hardware technologies stimulated the prosperities of the domains in spatial computing and deep learning algorithms, respectively. On one hand, advances in scalable and expressive neural network architectures and GPUs have paved the way to the recent breakthroughs in the deep learning field which has exhibited outstanding performance in handling data in space and time in specific domains such as image, audio, and video. On the other hand, the development and popularity of techniques in various domains such as remote sensing, online social media platforms, and bioengineering 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.
Nevertheless, 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. First, 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, which has been evidenced by the fast-increasing research work on spatiotemporal data using deep learning techniques in recent few years in the spatial data computing community. On the other hand, recently 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.
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, algorithms, and systems. Full research papers and short position papers will be accepted under the topics include, but not limited to, the following two broad categories:
Novel Deep Learning Techniques for Spatial and Spatio-Temporal Data:
Novel Deep Learning Applications for Spatial and Spatio-Temporal Data:
Liang Zhao, George Mason University
Xun Zhou, University of Iowa
Feng Chen, SUNY, Albany
August 24, 2019 August 30, 2019
Notification of Acceptance: September 6, 2019
Camera-ready Papers: September 8, 2019
Workshop Date: November 8, 2019
The workshop will encourage the submissions of both full research papers presents concrete research techniques and experimental results, as well as short position papers that identify and discuss the grand challenges and research opportunities on the topics of interests. All the workshop events will give enough time for attendant discussions. In particular, the workshop will consist of a series of the following events:
All manuscripts should be submitted in PDF format and formatted using the IEEE Proceedings templates available at: http://www.ieee.org/conferences_events/conferences/publishing/templates.html.
All the papers should be submitted through our online system here.
One author per accepted workshop contribution is required to register for the conference and workshop, to attend the workshop and to present the accepted submission. Otherwise, the accepted submission will not appear in the published workshop proceedings or in the workshop proceedings.
Liang Zhao (George Mason University): email@example.com
Xun Zhou (University of Iowa): firstname.lastname@example.org
Feng Chen(SUNY, Albany): email@example.com