Urban AI is an emerging field that combines AI, spatial computing, and urban science to address complex challenges faced by cities. The availability of extensive urban data and the growth of digitized city infrastructures have opened opportunities for data-driven machine learning approaches in urban science. Urban AI encompasses innovative AI techniques applied to urban problems, AI-ready urban data infrastructure, and various urban applications benefiting from AI. Its applications range from urban planning and design to traffic prediction, energy management, public safety, urban agriculture, and land use. This workshop aims to bring together researchers and practitioners to discuss advancements and future directions in urban AI.

Submission Guidelines

We solicit for regular papers (8-10 pages), short papers (4 pages), or position papers (2 pages) describing work-in-progress with innovative ideas related to the workshop topics. All papers must be original and not simultaneously submitted to another journal or conference.

Papers must be in ACM SIG format (US Letter size, 8.5 x 11 inches) including text, figures and references. Accepted papers will be published in the ACM digital library under the condition that at least one author has registered for both the main SIGSPATIAL conference and the workshop, attends the workshop, and presents the accepted paper in the workshop. Otherwise, the accepted paper will not appear in the workshop proceedings or in the ACM Digital Library version of the workshop proceedings.

List of Topics

• Core AI techniques for fostering the understanding of and decision-making in complex dynamic urban systems, such as:
  • Feature engineering and data augmentation
  • Prediction models
  • Control optimization, e.g., reinforcement learning
  • Transfer learning
  • Active learning
  • Generative AI (e.g., transformers, foundation models)
• AI as data-driven approaches for coupled modeling of urban systems and city digital twins, including:
  • Intelligent transportation systems
  • Built environment
  • Human dynamics and mobility
  • Urban planning
  • Urban socio-economic development
  • Emergency management
  • Climate
  • Synergistic relationships between built and natural environments
• Geospatial dimensions of urban networks, systems of cities, human dynamics, and built environment
  • GeoAI and location intelligence in urban context
  • Spatial data analytics for urban AI
  • Geospatial technologies and data infrastructure for urban AI
• Urban sciences applications empowered by AI such as:
  • AI-enabled urban mobility and transportation
  • AI-enabled smart cities and mobility management
  • AI-enabled urban social services
• Computational solutions to large-scale urban AI development and deployment
  • AI-empowered city digital twin technologies, urban sensing infrastructure, edge computing, Internet of Things (IoT) devices, and cyber-physical systems
  • Federated learning in multimodal urban systems
  • Machine learning (ML) infrastructure (MLOps, DataOps, and DevOps) solutions for large-scale urban AI
• Social and ethical issues of urban AI
  • Data quality and privacy concerns of AI in urban systems
  • AI biases in urban settings
  • Urban AI governance

Committees

Program Committee

• Prof. Chao Fan : Clemson University

• Prof. Filip Biljecki : National University of Singapore

• Prof. Vanessa Frias-Martinez : University of Maryland at College Park

• Dr. Hao Xuo : University of New South Wales, Sydney

• Prof. Chia-Yu Hsu : Arizona State University

• Prof. WenWen Li : Arizona State University

• Dr. Majbah Uddin : Oak Ridge National Laboratory

• Dr. Abhilasha Saroj : Oak Ridge National Laboratory

• Prof. Sukanya Randhawa : Heidelberg Institute for Geoinformation Technology, Heidelberg University, Germany

• Dr. Haoran Niu : Oak Ridge National Laboratory

• Dr. Soumendra Bhanja : Oak Ridge National Laboratory

• Dr. Haowen Xu : Oak Ridge National Laboratory

• Dr. Daniela Cialfi : Institute for Complex Systems, Council of National Research of Italy

• Prof. Hiba Baround : Vanderbilt University

Organizing committee

• Dr. Femi Omitaomu (omitaomuoa@ornl.gov) Senior R&D Staff and Group Leader, Computational Urban Sciences GroupOak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

• Prof. Ali Mostafavi (amostafavi@civil.tamu.edu) Associate Professor and Faculty Director, UrbanResilience.AI LabTexas A&M University, College Station, Texas, USA.

• Dr. Yan Liu (yanliu@ornl.gov) R&D Staff, Computational Urban Sciences GroupOak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

Contact

All questions about submissions should be emailed to urbanai@ornl.gov