This repository contains the implementation and material of the following paper:

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network
Weiyu Zhang, Yiyang Ma, Di zhu, Lei Dong, Yu Liu

Abstract: Simulating urban morphology with location attributes is a challenging task in urban science. Recent studies have shown that Generative Adversarial Networks (GANs) have the potential to shed light on this task. However, existing GAN-based models are limited by the sparsity of urban data and instability in model training, hampering their applications. Here, we propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN), for urban morphology simulation. We incorporate a progressive growing structure to learn hierarchical features and design a geographical loss to impose the constraints of water areas. Besides, we propose a comprehensive evaluation framework for the complex structure of urban systems. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics. Inspiringly, using physical geography features singly, MetroGAN can still generate shapes of the cities. These results demonstrate that MetroGAN solves the instability problem of previous urban simulation GANs and is generalizable to deal with various urban attributes.

• PDF link: https://arxiv.org/abs/2207.02590

The paper is accepted by KDD'22.

I'm sorry that due to my mistake, I previously uploaded an incorrect version of the dataset. Please use the latest version that has been corrected and reorganized (May 23rd).

• 百度网盘（Baidu Netdisk）: 链接: https://pan.baidu.com/s/1LlQtZrASIbhKVxkF6Fmnjg?pwd=0n67

The link of pretrained model: https://pan.baidu.com/s/1-IkFWgcC5t5UzlB_s19aZw?pwd=1234

Please consider citing our paper if this helps in your work:

Weiyu Zhang, Yiyang Ma, Di Zhu, Lei Dong, and Yu Liu. 2022. MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22). https://doi.org/10.1145/3534678.3539239