  Designing high-quality urban street layouts has long been in high demand, but
entangles notable challenges. Conventional methods based on deep generative
models are yet to fill the gap on integrating both natural and socioeconomic
factors in the design loop. In this paper, we propose a novel urban street
layout design method based on conditional adversarial learning. Specifically, a
conditional generative adversarial network trained on a real-world dataset
synthesizes street layout images from the feature map, into which an
autoencoder fuses a set of natural and socioeconomic data for a region of
interest; The following extraction module generates high-quality street layout
graphs corresponding to the synthesized images. Experiments and evaluations
suggest that the proposed method outputs various urban street layouts that are
visually and structurally alike their real-world counterparts, which can be
used to support the creation of high-quality urban virtual environments.
