No matter where in the world, local communities are largely shaped by residents — who themselves are, in turn, shaped by the communities in which they live. Community land use, infrastructure and resource allocation policies however are typically the product of models favoured by professional urban planners.
Urban planning deals with the physical layout of human settlements and guides orderly development in urban, suburban, and rural areas. Effective urban planning mitigates the operational and social vulnerabilities of an urban system, striving to improve quality of life while reducing traffic congestion and accidents, waste and pollution, and crime rates and tax burdens.
In the recent paper Reimagining City Configuration: Automated Urban Planning via Adversarial Learning, two University of Central Florida (UCF) PhD students specializing in spatiotemporal data mining, along with advisors from UCF, the Chinese University of Hong Kong and Virginia Tech, propose reducing the workloads of urban planners by introducing deep learning systems to handle some of their responsibilities.
“Traditional urban planning is time-consuming and laborious, and many factors need to be considered when generating the final planning scheme,” Dongjie Wang, a first-year UCF PhD student and first author of the paper, told Synced. “We wonder if AI can be used to automatically generate urban planning solutions.”