Clustering and Morphological Analysis of Campus Context

“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.