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, remote 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. More recently, advances in Artificial Intelligence and Machine Learning, particularly deep learning, remote sensing foundation models, earth embeddings, and agentic spatial reasoning have emerged as key enablers for extracting knowledge, supporting decision-making, and automating analytics over large-scale spatial and spatio-temporal data.

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”. The workshop will be organized in hybrid mode.

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:

Important Dates (Tentative)

Paper Submission Deadline October 15, 2026
Notification of Acceptance November 19, 2026
Camera-Ready Submissions December 1, 2026
Workshop Date December 14–17, 2026

Submission Guidelines and Instructions

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.

Submitted papers have to follow the IEEE official template. Maximum paper length allowed is:

Short paper titles should begin with “Short Paper:”
Demo paper titles should begin with “Demo Paper:”
Position/vision paper titles should begin with “Vision Paper:”

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.

Invited Keynote Speakers

As we did in previous years of this workshop, we plan to include a keynote and a panel in the workshop program. Details of the plans will be finalized upon acceptance of the workshop proposal.

Organizers

Workshop Co-Chairs

Farnoush Banaei-Kashani

farnoush.banaei-kashani@ucdenver.edu, University of Colorado Denver

Farnoush Banaei-Kashani is currently a tenured Associate Professor at the Department of Computer Science and Engineering, University of Colorado Denver (CU Denver). He is passionate about performing fundamental research toward building practical, large-scale data-intensive systems, with particular interest in Intelligent Data-driven decision-making Systems (IDSs), i.e., systems that automate the process of decision-making by applying data scientific solutions to (big) data. Dr. Banaei-Kashani has published more than 120 referred papers and has received several awards, including Best Paper awards from IEEE ICDM, ACM Sensys and IEEE CloudCom. He frequently serves as a senior program committee member in data science, AI and database conferences (including TKDE, ICLR, KDD, SIGMOD, VLDB, ICDE and SIGSPATIAL), and has also chaired many workshops and conferences over last few years, most recently the ACM SIGSPATIAL 2020 Conference. Dr. Banaei-Kashani’s research has been supported by grants from both governmental agencies (NIH, NSF, DOE, DOD, DOT, DoEd, DOJ and NASA) and industry (Google, IBM, Chevron, Intel and United Healthcare).

Abdeltawab Hendawi

hendawi@uri.edu, University of Rhode Island

Abdeltawab Hendawi is an Associate Professor in Computer Science and Data Science at the University of Rhode Island (URI). He is the Director of the AI-Lab for Research and Innovation at URI. He received his PhD in Computer Science from the University of Minnesota (UMN). Then, he was awarded a four-year Post Doctoral Research fellowship with Prof. John Stankovic in Computer Science at the University of Virginia (UVA). His research interests are centered on AI and Big Data with a focus on smart cities and smart health related applications. His work has been recognized by several awards at top ACM and IEEE conferences. His research is sponsored by grants from NSF, TIDC, USDA, P2P, and MindImmune Inc.

Ashwin Shashidharan

ashashidharan@esri.com, Esri

Ashwin Shashidharan is currently a Senior Software Developer at Esri with the GeoAnalytics team, where he actively works on developing scalable algorithms to analyze large spatiotemporal datasets. He holds MS and PhD degrees in Computer Science from North Carolina State University. His research interests include geospatial simulation, big spatial data management and distributed spatiotemporal analytics. Dr. Shashidharan has won several awards, including the Student Research Competition Award at the ACM SIGSPATIAL SRC 2016, and the Esri EDC International Student Award 2018. He has co-authored over 10 peer-reviewed papers, served on multiple review committees (SIGSPATIAL, ICDM, SSTD, BigData MDM, BDAC, SSTDM, ANNSIM, GeoIndustry, GeoSim, BigSpatial) and has served as co-chair for the ACM SIGSPATIAL BigSpatial workshop (2019-2026) and IEEE Big Spatial Data (BSD) workshop (2021-2026).

Program Committee

Webmaster

Chan Young Koh, ckoh04@uri.edu