Categories |
DEEP LEARNING
DATA MINING
|
About |
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. |
Call for Papers |
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:
Novel Applications of Deep Learning Techniques to Spatio-temporal Computing Problems. :
Novel Deep Learning Systems for Spatio-temporal Applications:
In addition, we encourage submissions of spatiotemporal deep learning methods that address problems related to the COVID-19 pandemic. |
Summary |
DeepSpatial 2020 : 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Sytems will take place in San Diego, CA, USA. It’s a 1 day event starting on Aug 24, 2020 (Monday) and will be winded up on Aug 24, 2020 (Monday). DeepSpatial 2020 falls under the following areas: DEEP LEARNING, DATA MINING, etc. Submissions for this Workshop can be made by May 20, 2020. Authors can expect the result of submission by Jun 15, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Jun 20, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the DeepSpatial 2020
|
Credits and Sources |
[1] DeepSpatial 2020 : 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Sytems |