There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from human's visual and intuitive perspective. We take the first step to bridge the gap by proposing a deep learning-based technique to automatically classify road networks into four classes on a visual basis. The method is implemented by generating an image of the street network (Colored Road Hierarchy Diagram), which we introduce in this paper, and classifying it using a deep convolutional neural network (ResNet-34). The model achieves an overall classification accuracy of 0.875. Nine cities around the world are selected as the study areas and their road networks are acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through a clustering on the percentage of each road network category. In the subsequent part of the paper, we focus on the usability of such classification: the effectiveness of our human perception augmentation is examined by a case study of urban vitality prediction. An advanced tree-based regression model is for the first time designated to establish the relationship between morphological indices and vitality indicators. A positive effect of human perception augmentation is detected in the comparative experiment of baseline model and augmented model. This work expands the toolkit of quantitative urban morphology study with new techniques, supporting further studies in the future.
翻译:由于人们越来越容易获得各种形式的空间大数据、增加计算能力以及利用从这类信息中受益的案例,目前存在着从数量上研究城市形态的趋势,因为人们越来越容易获得各种形式的空间大数据、增加计算能力以及使用从这类信息中受益的案例。迄今为止制定的方法用描述密度、比例和混合性的数字指数来衡量城市形态,但并不直接代表人类视觉和直观角度的形态特征。我们迈出第一步,提出一种深层次的基于学习的技术,将公路网络自动分类为视觉四类,从而缩小差距。该方法的实施方法是制作一条街道网络(Colored Road Higharchy Diagram)的图像,我们在本文中介绍,并利用一个深层革命神经网络(Res-34)对城市形态进行分类。该模型实现了0.875的总体分类准确性。世界各地的9个城市被选为研究区,其道路网络是从OpenStreetMap获得的。通过对每个公路网络类别的百分比进行汇总,进一步发现城市之间的深层分组。在本文的随后部分,我们侧重于支持这种分类:在人类生命力分析方面,我们未来的精确度分析模型的效益分析中,这是对城市结构变化变化的首次进行的一项研究。通过一个案例分析。