Big Spatial Data covers a wide spectrum of data types including (a) Raster data, e.g. Geoimages, (b) Vector data, e.g., Points, Lines, Polygons, and (c) Graph data, e.g. Road network graph. Thus, “Big Spatial Data” deals with massive amounts of real-time spatial and spatio-temporal data obtained from billions of sensors, location-aware devices, remoting sensing satellites, and various models of the physical world. The use of Big Spatial Data spans a variety of applications including social networks, earth sciences, transportation, communication networks, online maps, smart cities and urban planning, remote sensing, and crisis and evacuation management, to name but a few. Turning Big Spatial Data into value is challenging and requires introduction of fundamentally new spatio-temporal algorithms, methods, and systems that can store, mine, and analyze massive amounts of fast growing and heterogeneous spatio-temporal data in a timely manner.
The aim of this workshop is to bring together researchers and stakeholders from academia, government, and industry who are encountering and actively addressing problems in the area of Big Spatial Data management and analysis. This workshop offers the audience the opportunity to discuss the lessons which they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of “Big Spatial Data”.

Call for Papers

We encourage researchers from academia and industry to submit papers that highlight the value of Big Spatial Data processing, management, mining, and analysis on topics that include, but are not limited to the following

  • ● Big Spatial Data: Fundamentals and Theory
  • ● Big Spatial Data: Management
  • ● Big Spatial Data: Mining and Analysis
  • ● Big Spatial Data: Stream Processing
  • ● Big Spatial Data: Spatial Time Series Querying and Mining
  • ● Big Spatial Data: Spatial Graph Processing
  • ● Big Spatial Data: Descriptive, Predictive, and Prescriptive Models
  • ● Big Spatial Data: Privacy and Authentication
  • ● Big Spatial Data: Geosensing
  • ● Big Spatial Data: Indexing
  • ● Big Spatial Data: Modern Hardware, High Performance Computing, and Cloud
  • ● Big Spatial Data: Visualization
  • ● Big Spatial Data: Deep Learning
  • ● Big Spatial Data: NoSQL and NewSQL Data Stores
  • ● Big Spatial Data: Data Frameworks
  • ● Big Geospatial Information Retrieval and Crowdsourcing
  • ● Big Geosocial Networks
  • ● Big Spatial Data Applications: Smart Cities and Intelligent Transportation, Urban Computing, Healthcare, Geosciences, etc.

Important Dates

Paper Submission Deadline: October 1, 2020 October 15, 2020
Notification of Acceptance: November 1, 2020 November 15, 2020
Camera-Ready Submissions: November 15, 2020 November 20, 2020
Workshop Date: December 10-13, 2020


Authors with interests in any of the listed topics or any other topic related to Big Spatial Data paradigm are cordially invited to submit their work. All submissions should be in high quality, original and not published or under review elsewhere during the review process.

Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines as follows:
● 8.5" x 11" (DOC, PDF)
LaTex Formatting Macros

Maximum paper length allowed is:
Full Papers: 10 pages
Short (work-in-progress) Papers: 4 pages
Demo Papers: 4 pages
Position/Vision Papers: 4 pages

If from last three categoizes listed above, the title of the paper should start with “Short Paper:”, “Demo Paper:”, or “Vision Paper:”, respectively. Submitted papers will be reviewed by members of the Workshop Program Committee. At least one author for each accepted paper has to register and present the work.
Paper submission website: click here!

Keynote: Open Geospatial Data Machine Learning

Abstract: I will share my perspective on open data in the cloud, in particular open geospatial data, and present the Open Data Registry on AWS. The datasets range from genomics to climate to transportation information. They are well structured and easily accessible but there is a lack of sample scripts/notebooks for users to leverage these datasets in machine learning development. I will share my vision of a one-stop-shop on AWS where developers can easily access and contribute to the Open Data Registry and can leverage AWS broad and deep machine learning services and tools to develop and deploy machine learning models with sample notebooks on AWS SageMaker using the open datasets regardless of machine learning background. As proof of concept I will present a case study developing notebooks to train and test deep learning models using AWS SageMaker to extract building footprints and road networks from city scale satellite imagery and LiDAR data in the Open Data Registry. The notebooks reproduce winning algorithms from the SpaceNet challenges. In addition to the SpaceNet satellite images, we compare and combine USGS 3D Elevation Program (3DEP) LiDAR data to extract the buildings and roads.

Bio: Dr. Xin Chen is a Senior Manager in Amazon Machine Learning Solutions Lab. He leads his team to help AWS customers identify and build machine learning solutions to address their organization’s highest return-on-investment machine learning opportunities. Prior to Amazon Xin was a Director of Engineering at HERE Technologies whose team completed pioneering work to achieve the automation of next generation map creation using computer vision and machine learning technologies. Xin has over 50 U.S. Patents and numerous publications at CVPR, CVIU and IEEE Transactions on ITS. He has served on an NSF (National Science Foundation) panel multiple times to evaluate and award funding to multi-million dollar projects advancing AI research. Xin is an adjunct faculty at Northwestern U. and Illinois Institute of Technology teaching “Geospatial Vision and Visualization” and “Biometrics” courses. Xin obtained his Ph.D. in Computer Science and Engineering from the U. of Notre Dame.


Note that all times are in USA Eastern Standard Time (EST).
Start (EST) End (EST) Event
9:00 9:05 Opening
9:05 10:25 Paper Presentation Session #1, Session Chair: Dr. Farnoush Banaei-Kashani
9:05 9:25 Ad Recommendation Utilizing User Behavior in The Physical Space to Represent Their Latent Interest
  • ● Takanobu Omura, Kenta Suzuki, Panote Siriaraya, Mohit Mittal, Yukiko Kawai, and Shinsuke Nakajima
9:25 9:55 Crowd Forecasting at Venues with Microblog Posts Referring to Future Events
  • ● Ryotaro Tsukada, Haosen Zhan, Shonosuke Ishiwatari, Masashi Toyoda, Kazutoshi Umemoto, Haichuan Shang, and Koji Zettsu
9:55 10:25 Toward Identifying the Urban Community Structure From Population Flow and Public Services Distribution
  • ● Qinghe Liu, Zhicheng Liu, Yinfei Xu, Weiting Xiong, Junyan Yang, and Qiao Wang
10:25 10:45 Coffee Break
10:45 12:05 Paper Presentation Session #2, Session Chair: Dr. Abdeltawab Hendawi
10:45 11:15 Country-wide Mobility Changes Observed Using Mobile Phone Data During COVID-19 Pandemic
  • ● Georg Heiler, Tobias Reisch, Jan Hurt, Mohammad Forghani, Aida Omani, Allan Hanbury, and Farid Karimipour
11:15 11:45 Real-time Traffic Jam Detection and Congestion Reduction Using Streaming Graph Analytics
  • ● Zainab Abbas, Paolo Sottovia, Mohamad Al Hajj Hassan, Daniele Foroni, and Stefano Bortoli
11:45 12:05 Leveraging Differential Privacy in Geospatial Analyses of Standardized Healthcare Data
  • ● Daniel Harris
12:05 12:25 Coffee Break
12:25 13:25 Keynote
  • ● Xin Chen
13:25 13:30 Closing Remarks


See the IEEE Big Data 2020 Registration Page.


See the IEEE Big Data 2020 Accommodations Page.


Workshop Co-chairs

Farnoush Banaei-Kashani,, University of Colorado Denver

Farnoush Banaei-Kashani is currently an assistant professor at the Department of Computer Science and Engineering, College of Engineering and Applied Science, University of Colorado Denver. Previously, he was a research scientist at the Computer Science Department, University of Southern California (USC), where he also earned his PhD degree in Computer Science and MS degree in Electrical Engineering in 2007 and 2002, respectively. Dr. Banaei-Kashani is passionate about performing fundamental research toward building practical, large-scale data-intensive systems, with particular interest in Data-driven Decision-making Systems (DDSs), i.e., systems that automate the process of decision-making based on (big) data. In the past he has introduced a number of novel DDSs in various application areas (including, transportation, health, safety and security, energy, and scientific computing). Dr. Banaei-Kashani has published more than 65 referred papers and has received several awards. His research has been supported by grants from both governmental agencies (NSF/CENS, NIH/CTSI, DOT/METRANS, DOJ/NIJ and NASA/JPL) and industry (Google, IBM, Chevron and NGC).

Siyuan Lu,, IBM

Siyuan Lu is currently a Manager and Principal Research Staff Member leading the Data Intensive Physical Analytics group at the IBM T. J. Watson Research Center, Yorktown Heights, NY. He received the Ph.D. degree in Physics from University of Southern California (USC) in 2006. Prior to joining IBM in 2012, he was an Assistant Research Professor jointly appointed in the Department of Physics and the Department of Ophthalmology at USC. Dr. Lu’s current research interests at IBM include architectures of geospatial data services and the union of physics and data-driven approaches for modeling complex systems with applications in renewable energy forecasting, climate forecasting, and environmental monitoring. He technically leads the Department of Energy funded project on solar energy forecasting and the development of IBM PAIRS geospatial Big Data services. He has co-authored over 50 peer-reviewed articles and has served as members on multiple governmental committees.

Chengyang Zhang,, Amazon

Chengyang Zhang is currently a Software Development Manager at leading a team across retail and AWS on PartiQL - A SQL-compatible access to relational, semi-structured, and nested data. He completed his PhD in Computer Science from the University of North Texas in 2011. Dr. Zhang is actively working on various projects related to database and machine learning systems. His research interests includes query processing, spatial databases and bridging database with ML systems. Dr. Zhang has published over 20 peer reviewed papers and owns 5 patents.

Abdeltawab Hendawi,, University of Rhode Island

Abdeltawab Hendawi is currently an Assistant Professor in the Department of Computer Science and Statistics at the University of Rhode Island, and the Co-director of the AI-Lab at URI. He obtained my MSc and PhD in Computer Science and Engineering from the University of Minnesota, Twin Cities. His research interests are in big data management and analytics with applications in smart city and smart health. Dr. Hendawi has won a number of awards, including the Best Paper Award at the ACM SIGSPATIAL MobiGIS 2012, the Best Design/Plan Poster Award at the U-Spatial Symposium 2013; Best Overall Poster Award, also at the U-Spatial Symposium 2013; the Best Demo Paper Award at the ACM SIGSPATIAL 2014; and Best and Second Best Demo Paper Awards at the IEEE Mobile Data Management (MDM) 2015. My vision for the future research challenges in the area of location-aware services also won a highly recognized Award at the ACM SIGSPATIAL 2016. In addition, he is the recipient of the Hobby Postdoctoral Research Fellowship 2015-2018 from the Computer Science Department at the University of Virginia, and the Best Poster Award at the UVa Research Symposium 2018; and Best Demo Paper Award Runner-up at ACM SIGSPATIAL 2018. He has been a co-chair for the IEEE Big Spatial Data workshop (BSD) 2016 to 2019.

Program Committee

● Louai Alarabi, Umm Al Qura University
● Ahmed Eldawy, University of California Riverside
● Shahab Helmi, Terumo BCT
● Xiao Liu, University of Arkansas
● Amr Magdy, University of California, Riverside
● Fernando Marianno, IBM T. J. Watson
● Carlo Siebenschuh, IBM
● Fusheng Wang, Stony Brook University
● Jianting Zhang, City College of New York and CUNY Graduate Center
● Demetris Zeinalipour, University of Cyprus


● Shahab Helmi,