  Urban planning refers to the efforts of designing land-use configurations
given a region. However, to obtain effective urban plans, urban experts have to
spend much time and effort analyzing sophisticated planning constraints based
on domain knowledge and personal experiences. To alleviate the heavy burden of
them and produce consistent urban plans, we want to ask that can AI accelerate
the urban planning process, so that human planners only adjust generated
configurations for specific needs? The recent advance of deep generative models
provides a possible answer, which inspires us to automate urban planning from
an adversarial learning perspective. However, three major challenges arise: 1)
how to define a quantitative land-use configuration? 2) how to automate
configuration planning? 3) how to evaluate the quality of a generated
configuration? In this paper, we systematically address the three challenges.
Specifically, 1) We define a land-use configuration as a
longitude-latitude-channel tensor. 2) We formulate the automated urban planning
problem into a task of deep generative learning. The objective is to generate a
configuration tensor given the surrounding contexts of a target region. 3) We
provide quantitative evaluation metrics and conduct extensive experiments to
demonstrate the effectiveness of our framework.
