1 research outputs found
Classification of Urban Morphology with Deep Learning: Application on Urban Vitality
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 the 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 with their road networks acquired from
OpenStreetMap. Latent subgroups among the cities are uncovered through
clustering on the percentage of each road network category. In the subsequent
part of the paper, we focus on the usability of such classification: we apply
our method in a case study of urban vitality prediction. An advanced tree-based
regression model (LightGBM) is for the first time designated to establish the
relationship between morphological indices and vitality indicators. The effect
of road network classification is found to be small but positively associated
with urban vitality. This work expands the toolkit of quantitative urban
morphology study with new techniques, supporting further studies in the future