Overview 2024
The 2nd ACM SIGSPATIAL International Workshop on Advances in Urban AI 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.
In the era of digital transformation, cities are becoming smarter, more sustainable, and efficient. Urban AI leverages data collected from sensors, satellites, and IoT devices to enable evidence-based decision making. Real-time analysis of patterns, infrastructure performance, energy consumption, and social dynamics helps identify vulnerabilities, optimize resource allocation, and inform 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 urban AI R&D to address geospatial challenges in urban environments.
Call for Papers
The 2024 Urban-AI workshop invites papers in the following topics (but not limited to):
- Core AI techniques originated from and/or applicable to urban environment
- Spatial data analytics for urban AI
- Geospatial technologies and data infrastructure for urban AI
- AI-enabled urban mobility and transportation
- AI-enabled urban environmental management
- AI-enabled urban social services
- Generative AI and risk-informed urban planning
- Ethical and legal issues of AI in urban planning and management
- Data quality and privacy concerns of AI in urban planning and management
- Case studies and best practices of AI applications in urban planning and management
Workshop Organizers
Dr. Femi Omitaomu ([email protected]; +1-865-241-4310)
Group Leader and Distinguished Scientist, Computational Urban Sciences Group
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
Prof. Ali Mostafavi ([email protected]; +1-765-543-4036)
Associate Professor and Faculty Director, UrbanResilience.AI Lab
Texas A&M University, College Station, Texas, USA.
Dr. Sukanya Randhawa ([email protected]; +49-176-2284799)
Heidelberg Institute for Geoinformation Technology
Heidelberg University, Germany
Dr. Haoran Niu ([email protected]; +1- 865-341-0247)
Post-doc, Computational Urban Sciences Group
Oak 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
- Yanjie Fu : Arizona State University
- Janet Agbaje, Oak Ridge National Laboratory
Student Member
- Min Namgung: University of Minnesota