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Urban planning refers to the efforts of designing land-use configurationsgiven a region. However, to obtain effective urban plans, urban experts have tospend much time and effort analyzing sophisticated planning constraints basedon domain knowledge and personal experiences. To alleviate the heavy burden ofthem and produce consistent urban plans, we want to ask that can AI acceleratethe urban planning process, so that human planners only adjust generatedconfigurations for specific needs? The recent advance of deep generative modelsprovides a possible answer, which inspires us to automate urban planning froman adversarial learning perspective. However, three major challenges arise: 1)how to define a quantitative land-use configuration? 2) how to automateconfiguration planning? 3) how to evaluate the quality of a generatedconfiguration? In this paper, we systematically address the three challenges.Specifically, 1) We define a land-use configuration as alongitude-latitude-channel tensor. 2) We formulate the automated urban planningproblem into a task of deep generative learning. The objective is to generate aconfiguration tensor given the surrounding contexts of a target region. 3) Weprovide quantitative evaluation metrics and conduct extensive experiments todemonstrate the effectiveness of our framework.

Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation