Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
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DOI：
10.1145/3397536.3422268
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Dongjie Wang;Yanjie Fu;Pengyang Wang;B. Huang;Chang-Tien Lu
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文献类型：
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作者：
Dongjie Wang;Yanjie Fu;Pengyang Wang;B. Huang;Chang-Tien Lu
城市规划是指设计土地使用配置的努力。有效的城市规划有助于减轻城市系统的运营和社会脆弱性，如高税收、犯罪、交通拥堵和事故、污染、抑郁和焦虑。由于城市系统的高度复杂性，这些任务大多由专业规划师完成。但是，人类规划者需要更长的时间。深度学习的最新进展促使我们提出这样的问题：机器是否可以像人类一样自动快速地计算土地使用配置，以便人类规划者最终能够根据特定需求调整机器生成的规划？为此，我们制定了自动化的城市规划问题，学习配置土地使用的任务，鉴于周围的空间环境。为了设置任务，我们定义了一个土地使用配置作为一个纬度通道张量，其中每个通道是一个类别的POI和一个条目的值是POI的数量。然后，我们的目标是提出一个对抗性学习框架，可以自动生成这样的张量为一个计划外的区域。特别是，我们首先通过使用地理和人类移动数据从空间图中学习表示来表征未规划区域周围区域的上下文。其次，我们将每个未规划区域及其周围的上下文表示组合为元组，并将所有元组分类为阳性（规划良好的区域）和阴性样本（规划不良的区域）。第三，我们开发了一种对抗性的土地使用配置方法，其中周围的上下文表示被馈送到生成器中以生成土地使用配置，并且学习器学习区分正样本和负样本。最后，我们设计了两种新的测量方法来评估土地利用配置的质量，并提出了广泛的实验和可视化结果来证明我们的方法的有效性。
Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples. Finally, we devise two new measurements to evaluate the quality of land-use configurations and present extensive experiment and visualization results to demonstrate the effectiveness of our method.