The 2^nd ACM SIGSPATIAL International Workshop on Advances in Urban AI (UrbanAI) brings together researchers and practitioners to discuss advancements and future directions in urban AI. UrbanAI 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.

In the era of digital transformation, cities are becoming smarter, more sustainable, and efficient. UrbanAI leverages data collected from sensors, satellites, and IoT devices to enable evidence-based decision making. Real-time analysis of climate patterns, infrastructure performance, energy consumption, and social dynamics helps identify vulnerabilities, optimize resource allocation, and inform climate resilience strategies. Urban AI R&D takes this concept to new heights by utilizing advanced AI algorithms, machine learning, and extensive data analytics to establish intelligent urban ecosystems. These ecosystems integrate various urban domains, fostering cohesive operations, data-informed decision making, and improved citizen experiences. Considering the complexity and heterogeneity of urban information, spatiotemporal aspects such as location, distance, shape, and spatial patterns must be carefully considered and incorporated into UrbanAI R&D to address geospatial challenges in urban environments.

Submission Guidelines

The following paper categories are welcome:

Full research papers: 8-10 pages (A full research paper will present a specific problem or topic and discuss methodology and findings along with future research directions)

Short research papers or application demo papers: 4 pages (Short research papers will demonstrate existing methods, toolkits, and best practices for building intelligent and resilient cities)

Vision or statement papers: 2 pages (Vision paper will be more situational in their coverage)

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.

Workshop Organizers

• Dr. Femi Omitaomu (omitaomuoa@ornl.gov; +1-865-241-4310)Group Leader and Distinguished Scientist, Computational Urban Sciences GroupOak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
• Prof. Ali Mostafavi (amostafavi@civil.tamu.edu; +1-765-543-4036)Associate Professor and Faculty Director, UrbanResilience.AI LabTexas A&M University, College Station, Texas, USA.
• Dr. Sukanya Randhawa (Sukanya.Randhawa@heigit.org; +49-176-2284799)Heidelberg Institute for Geoinformation TechnologyHeidelberg University, Germany
• Dr. Haoran Niu (niuh@ornl.gov; +1- 865-341-0247)Post-doc, Computational Urban Sciences GroupOak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

Program Committee

• Chao Fan : Clemson University
• Filip Biljecki : National University of Singapore
• Vanessa Frias-Martinez : University of Maryland at College Park
• Hao Xue : University of New South Wales, Sydney
• Chia-Yu Hsu : Arizona State University
• WenWen Li : Arizona State University
• Majbah Uddin : Oak Ridge National Laboratory
• Abhilasha Saroj : Oak Ridge National Laboratory
• Steffen Knoblauch : Heidelberg Institute for Geoinformation Technology, Heidelberg University, Germany
• Daniela Cialfi : Institute for Complex Systems, Council of National Research of Italy
• Hiba Baround : Vanderbilt University
• Yang Chen : Oak Ridge National Laboratory

Student Member

• Min Namgung: University of Minnesota

Contact

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