论文标题
Metrogan：通过生成对抗网络模拟城市形态
MetroGAN: Simulating Urban Morphology with Generative Adversarial Network
论文作者
论文摘要
使用位置属性模拟城市形态是城市科学的一项艰巨任务。最近的研究表明，生成的对抗网络（GAN）有可能阐明这项任务。但是，现有的基于GAN的模型受到城市数据的稀疏性和模型培训的不稳定的限制，从而阻碍了他们的应用。在这里，我们为城市形态模拟提出了一个带有地理知识的GAN框架，即Metrogan（Metrogan）。我们结合了一个渐进式的结构，以学习分层特征并设计地理损失，以施加水域的限制。此外，我们为城市系统的复杂结构提出了一个全面的评估框架。结果表明，在所有指标中，Metrogan的表现都超过20％，胜过最先进的城市模拟方法。鼓舞人心的是，使用物理地理特征，Metrogan仍然可以产生城市的形状。这些结果表明，Metrogan解决了以前的城市模拟甘斯的不稳定性问题，并且可以推广以处理各种城市属性。
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.
