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.
|Original language||English (US)|
|Title of host publication||KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||11|
|State||Published - Aug 14 2022|
|Event||28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States|
Duration: Aug 14 2022 → Aug 18 2022
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Conference||28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022|
|Period||8/14/22 → 8/18/22|
Bibliographical noteFunding Information:
This research was funded by the National Natural Science Foundation of China (41830645, 41971331). Dr. Di Zhu is supported by the Faculty Set-up Funding of College of Liberal Arts, University of Minnesota (1000-10964-20042-5672018).
© 2022 ACM.
- generative adversarial networks
- urban morphology simulation