UrbanAI’26: The 4th ACM SIGSPATIAL International Workshop on Advances in Urban AI UrbanAI’26: The 4th ACM SIGSPATIAL International Workshop on Advances in Urban AI

Co-located with the 34th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2026
November 3, 2026 | Riverside, California, USA
Workshop website: https://urbanai.ornl.gov/

BackgroundBackgroundBackgroundBackground

The 4th ACM SIGSPATIAL International Workshop on Advances in Urban AI (UrbanAI’26) brings together researchers and practitioners to discuss recent advances, emerging challenges, and future directions in Urban AI. UrbanAI’26 is co-located with the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2026, which will be held in Riverside, California, USA, in November 2026.

As cities continue to undergo digital transformation, they are becoming increasingly connected, adaptive, and data-driven. Urban AI plays a central role in this transformation by leveraging data from diverse sources, including sensors, satellites, mobile devices, Internet of Things systems, simulation platforms, and large-scale geospatial datasets. These data streams support real-time and long-term analysis of critical urban functions, including infrastructure performance, mobility patterns, energy consumption, environmental risk, public services, and human activity.

Recent advances in artificial intelligence, including foundation models, generative AI, agentic AI, graph learning, spatiotemporal modeling, and hybrid simulation-AI systems, provide new opportunities to understand, predict, and optimize complex urban systems. At the same time, Urban AI faces significant challenges related to heterogeneous data, privacy, uncertainty, scalability, interpretability, and deployment in real-world urban environments. These challenges are inherently spatial and temporal, requiring methods that can reason about location, proximity, topology, networks, scale, and dynamic interactions.

UrbanAI’26 aims to provide a focused forum for exchanging ideas, methods, applications, and visions at the intersection of AI, geospatial information science, and urban systems. The workshop welcomes contributions that advance both the methodological foundations and practical applications of AI for smarter, safer, more sustainable, and more resilient cities.

Call for PapersCall for PapersCall for PapersCall for Papers

The UrbanAI’26 workshop invites submissions on topics including, but not limited to:

Core Urban AI Topics

1. Foundational and Emerging AI Paradigms

• Foundational AI models for urban systems
• Agentic AI for urban applications
• Hybrid AI–quantum computing for urban-scale problems (optimization, simulation, sensing, and secure communications)

2. Urban Systems, Infrastructure, and Mobility

• AI for urban infrastructure planning, management, and operations
• AI-enabled urban mobility, transportation, and logistics systems
• AI for siting, planning, and optimization of urban data centers and computing infrastructure

3. Resilience, Sustainability, and Environmental Intelligence

• AI for urban resilience, disaster response, and environmental risk management
• AI for thermal, environmental, and energy impact modeling in urban systems
• Energy-efficient and sustainable AI for urban deployments

4. Sensing, Data, and Urban Intelligence

• AI for urban sensing, situational awareness, and real-time monitoring
• AI for data quality, privacy, and reliability in urban sensor networks
• AI-enabled interpretation of multimodal sensing (including emerging paradigms such as quantum sensing)

5. Edge, Distributed, and Scalable Urban AI Systems

• Edge computing and real-time AI architectures for urban environments
• AI workload optimization and distributed computing for urban-scale systems
• Resilient and scalable AI infrastructure for urban continuity and disaster recovery

6. Urban Analytics and Decision Intelligence

• AI-enhanced location-based services and geospatial intelligence
• Spatiotemporal modeling and urban digital twins
• Benchmarking and evaluation of classical, AI, and hybrid methods for urban decision-making

Submission GuidelinesSubmission GuidelinesSubmission GuidelinesSubmission Guidelines

The following paper categories are welcome:

Full research papers: 8–10 pages
Full research papers should present a specific research problem or application area, describe the methodology, report findings, and discuss implications and future research directions.

Short research papers or application/demo papers: 4 pages
Short papers and demo papers may present preliminary results, existing methods, toolkits, operational systems, datasets, case studies, or best practices for building intelligent, sustainable, and resilient cities.

Vision or statement papers: 2 pages
Vision papers should present forward-looking perspectives, emerging challenges, new research directions, or position statements related to Urban AI.

Papers must be prepared in the double-column ACM SIG format using US Letter size, 8.5 × 11 inches, including text, figures, tables, and references. Accepted papers will be published in the ACM Digital Library, provided that at least one author registers for both the main SIGSPATIAL conference and the workshop, attends the workshop, and presents the accepted paper at the workshop. Otherwise, the accepted paper will not appear in the workshop proceedings or the ACM Digital Library version of the proceedings.

DeadlinesDeadlinesDeadlinesDeadlines

• Paper Submission: Friday, Aug 21, 2026 (11:59 PM PDT)
• Notification of Accept/Reject: Friday, September 11, 2026 (11:59 PM PDT)
• Camera-ready: Friday, September 25, 2026 (11:59 PM PDT)

Workshop OrganizersWorkshop OrganizersWorkshop OrganizersWorkshop Organizers

• Dr. Haoran Niu (niuh@ornl.gov) Research Scientist, Computational Urban Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
• Prof. Hao Xue (hao.xue1@unsw.edu.au) Assistant Professor, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia.
• Dr. Femi Omitaomu (omitaomuoa@ornl.gov) Group Leader and Distinguished Scientist, Computational Urban Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
• Prof. Liang Zhao (liang.zhao@emory.edu) Associate Professor, Department of Computer Science, Emory University, Atlanta, USA.
• Prof. Yanjie Fu (yanjie.fu@asu.edu) Associate Professor, School of Computing and AI, Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, USA.
• Prof. Yao-Yi Chiang (yaoyi@umn.edu), Associate Professor, Computer Science & Engineering, Director of Graduate Studies, Data Science, University of Minnesota, Twin Cities, MN, USA.
• Prof. Hiba Baroud (hiba.baroud@vanderbilt.edu), Associate Professor, Civil & Environmental Engineering, Deputy Director, Vanderbilt Center for Sustainability, Energy, and Climate, Vanderbilt University, Nashiville, Tennessee, USA.
• Ms. Min Namgung (namgu007@umn.edu), Ph.D. Candidate, Computer Science, University of Minnesota, Twin Cities, MN, USA
• Ms. Yijun Lin (lin00786@umn.edu) Ph.D. Candidate, Computer Science, University of Minnesota, Twin Cities, MN, USA

Program CommitteeProgram CommitteeProgram CommitteeProgram Committee

• Prof. Zhaonan Wang: Urban Studies and Computer Science at NYU Shanghai
• Prof. Ayan Mukhopadhyay: the College of William & Mary
• Prof. Hemant Purohit: George Mason
• Dr. Abhilasha Saroj : Oak Ridge National Laboratory
• Dr. Yang Chen: Oak Ridge National Laboratory
• Dr. Soumendra Bhanja: Oak Ridge National Laboratory
• Dr. Bharat Sharma: Oak Ridge National Laboratory
• Dr. Daniela Cialfi : Institute for Complex Systems, Council of National Research of Italy
• Jina Kim: University of Minnesota
• Prof. Hiba Baround : Vanderbilt University
• Dr. Edward Dong: the University of New South Wales
• Dr. Yang Yang: Researcher, the University of New South Wales
• Dr. Arian Prabowo: Postdoctoral Researcher, School of Computer Science and Engineering, University of New South Wales
• Min Namgung: Ph.D. Candidate in Computer Science, University of Minnesota
• Wilson Wongso: PhD candidate in Computer Science at the University of New South Wales
• Lihua Li: PhD student, CSE, University of New South Wales
• Du Yin: PhD student, CSE, University of New South Wales