  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.
