Urban AI @ ORNL

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 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.

Researchers at Oak Ridge National Laboratory are developing AI algorithms for optimizing land use, predicting urban planning, designing resilient infrastructure, and improving resource allocation, thus enhancing urban sustainability and livability.