A novel unsupervised deep learning method for the generalization of urban form

Abstract

ABSTRACT Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate. Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels. Due to the complexity of urban systems, it is challenging to consistently define urban form types and collect metadata to describe them. Therefore, in this study, we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional supervised urban form classification methods. The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder (MRCAE), which can learn the latent representation of different urban form types. These vectors can be further used to generalize urban form types by using Self-Organizing Map (SOM) and the Gaussian Mixture Model (GMM). The proposed method is applied in the metropolitan area of Guangzhou-Foshan, China. The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images. The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets, including building footprints, Points-of-Interests (POIs) and Tencent User Density (TUD) data. The results reveal that the urban form map generated based on the MRCAE can explain 55% of the building height distribution and 55% of the building area distribution, which are 2.1% and 3.3% higher than those derived from the conventional convolutional autoencoder. As the information of urban form is essential to urban climate models, the results presented in this study can become a basis to refine the quantification of urban climate parameters, thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.