Traditional Chinese Village Morphological Feature Extraction and Cluster Analysis Based on Multi-source Data and Machine Learning

Abstract

This study of traditional village morphology provides a possible entry point for understanding the growth patterns of settlements for sustainable development. This study proposes a hybrid data-driven approach to support quantitative morphological descriptions and to further morphology-related studies using opensource map data and deep learning approaches. We construct a dataset of 6819 traditional villages on the Chinese official list with geometrical, geographic and related no-material information. The images containing village buildings combined with roads or other environments are represented in binary to explore the integrated influence of these elements. The neural network is implemented to quantify the morphological features into feature vectors. After dimension reduction, cluster analysis is conducted by calculating the distance between the feature vectors to reveal five main types of Chinese traditional village patterns. The proposed method considers their overall spatial form and other factors such as size, transportation, graphical structure, and density. At the same time, it explores a framework using machine learning in the conservation and renewal work. And it also shows the possibility of data-driven methods for design and decision making.