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.
Guangyin Jin, Hengyu Sha, Yanghe Feng, Cheng Qing and Huang Jincai.
Tin Vu and Ahmed Eldawy. 5. Generate Street Map Images from Satellite Images and Crowd-sourced Geographic data using GAN
5. Generate Street Map Images from Satellite Images and Crowd-sourced Geographic data using GAN
(All times are in PDT time zone)
|1:00pm-1:05pm||Opening and Welcome|
|1:05pm-1:35pm||Keynote 1 by Dr. Zhenhui (Jessie) Li, PSU
|1:35pm-2:05pm||Keynote 2 by Dr. Jing Dai, Google
|2:05pm-2:29pm||Paper Splotlight Presentation (All the presentation videos are available here):
|2:29pm-2:45pm||Poster Breakout Room|
|2:45pm-3:15pm||Keynote 3 by Dr. Ranga Raju Vatsavai, NCSU
|3:15pm-4:45pm||Panel “The future of AI for spatiotemporal data science”
Panelists: Dr. Wei Ding (NSF), Dr. Wei-Shinn Ku (NSF), Dr. Shashi Shekhar (UMN), and Dr. Amarda Shehu (NSF).
This talk presents how to utilize mobility data and advanced learning methods for traffic signal control. First, I will examine the existing traffic signal control system and discuss why today we have the opportunity for a potential breakthrough in traffic signal control. Second, the talk presents our recent research results in traffic signal control via reinforcement learning which are published in recent KDD, CIKM, and AAAI conferences. Finally, I would like to discuss the open challenges in this research topic and its implications for smart city applications.
Dr. Zhenhui (Jessie) Li is a tenured associate professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Her research has been focused on mining spatial-temporal data with applications in transportation, ecology, environment, social science, and urban computing. She is a passionate interdisciplinary researcher and has been actively collaborating with cross-domain researchers. To learn more, please visit her homepage: https://faculty.ist.psu.edu/jessieli
The Maps industry has been the frontline of applied machine learning techniques, especially for generating maps data from imagery. This talk is going to provide an overview to the Maps features and data at Google that rely on deep learning techniques, to introduce the recent efforts on improving Maps for better user experiences for navigation and assistant driving, and to discuss the challenges that the engineering teams have faced when applying deep learning to build Maps.
Dr. Jing (David) Dai is an experienced researcher and senior engineer on spatial and spatiotemporal data management and data mining. He is currently an Engineering Lead at Google where he joined in 2011, leading the efforts to generate Maps data for Auto. He received his Ph.D. degree in Computer Science from Virginia Tech in 2009. Dr. Dai was a Research Scientist at IBM T.J. Watson Research Center from 2009 to 2011, where he worked on building the first Smart City in the US. He has served on the committee for several international conferences and workshops, as well as the NSF panels.
Global earth observations with constellation of more than 100 operational satellites are providing unprecedented spatiotemporal data coverage, which can be exploited to continuously monitor key resources. Earth is a dynamical system continually changing due to both natural and human induced factors. Recent decade has witnessed major changes on the Earth, for example, deforestation, varying cropping and human settlement patterns, and crippling damages due to disasters. Monitoring this dynamic phenomenon is critical for human wellbeing. In this talk we will explore recent advances in AI and machine learning for monitoring natural and as well as man-made structures at regional and global scales.
Dr. Raju Vatsavai is a Chancellor’s Faculty Excellence Program Cluster Associate Professor in the Computer Science department at the North Carolina State University. He works at the intersection of spatial and temporal big data management, analytics, and high-performance computing with applications in the national security, geospatial intelligence, natural resources, climate change, location-based services, and human terrain mapping. As the Associate Director of the Center for Geospatial Analytics (CGA), Raju plays a leadership role in the center’s strategic vision for spatial computing research. He has published more than 100 peer-reviewed articles in conferences and journals and edited two books on “Knowledge Discovery from Sensor Data.” He served on program committees of leading international conference including ACM KDD, ACM GIS, ECML/PKDD, SDM, CIKM, IEEE BigData, and co-chaired several workshops including ICDM/SSTDM, ICDM/KDCloud, ACM SIGSPATIAL BigSpatial, Supercomputing/BDAC, KDD/LDMTA, KDD/Sensor-KDD, and SDM/ACS. He holds MS and PhD degrees in computer science from the University of Minnesota. At present, he is on leave from the NCSU and working as Distinguished Research Fellow at the Lirio.
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