The 4th ACM SIGSPATIAL International Workshop on Advances in Urban AI (UrbanAI’26) 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.

Call for Papers:

The Urban-AI 2026 workshop invites papers in the following topics (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