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Modeling urbanization patterns with generative adversarial networks

Adrian Albert, Emanuele Strano, Jasleen Kaur, Marta Gonzalez

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.

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