  In this study we propose a new method to simulate hyper-realistic urban
patterns using Generative Adversarial Networks trained with a global urban
land-use inventory. We generated a synthetic urban "universe" that
qualitatively reproduces the complex spatial organization observed in global
urban patterns, while being able to quantitatively recover certain key
high-level urban spatial metrics.

Machine Learning