Overview 2025
The 3rd ACM SIGSPATIAL International Workshop on Advances in Urban AI (UrbanAI’25) brings together researchers and practitioners to discuss advancements and future directions in urban AI. 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.
As cities embrace digital transformation, they are evolving into smarter, more resilient, and efficient urban environments. At the heart of this evolution is Urban AI, which leverages data from a wide array of sources, including sensors, satellites, and IoT devices, to enable real-time analysis of critical urban functions. These data streams offer insights into infrastructure performance, urban system variability, energy usage, and human activity, providing a foundation for evidence-based decisions in urban planning, emergency management, and sustainability efforts. Advancements in Urban AI research push this vision further by employing state-of-the-art AI models, advanced analytics, and simulation technologies to build intelligent urban ecosystems. These ecosystems seamlessly integrate data across diverse domains such as transportation, energy, housing, and public services, fostering coordinated, efficient, and citizen-focused urban operations. A key challenge lies in the heterogeneous nature of urban data, which demands a nuanced understanding of geospatial characteristics, including location, proximity, topology, and spatiotemporal patterns. As such, spatiotemporal reasoning stands as a cornerstone of Urban AI, underscoring its significance to the SIGSPATIAL community.
Program and Committee Members 2025
Program Committee
Prof. Dongsheng Ding: University of Tennessee, Knoxville
Dr. Majbah Uddin : Oak Ridge National Laboratory
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. Steffen Knoblauch : Heidelberg Institute for Geoinformation Technology, Heidelberg University, Germany
Dr. Daniela Cialfi : Institute for Complex Systems, Council of National Research of Italy
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
Janet Agbaje, Oak Ridge National Laboratory