Classification and Distribution of Traditional Grass-Roofed Dwellings in China Based on Deep Learning

Jin Tao, Yuxin Zeng, Xiaolan Zhuo (), Zhibo Wang, Jihang Xu and Peng Ren
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Jin Tao: School of Architecture, South China University of Technology, Guangzhou 510641, China
Yuxin Zeng: School of Architecture, South China University of Technology, Guangzhou 510641, China
Xiaolan Zhuo: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Zhibo Wang: School of Architecture, South China University of Technology, Guangzhou 510641, China
Jihang Xu: School of Architecture, South China University of Technology, Guangzhou 510641, China
Peng Ren: School of Architecture, South China University of Technology, Guangzhou 510641, China

Land, 2024, vol. 13, issue 10, 1-34

Abstract: Traditional grass-roofed dwellings are important components of Chinese vernacular architecture. Building a comprehensive nationwide database of traditional grass-roofed dwellings is crucial for the inherence of this cultural heritage and its traditional ecological technologies. This study proposes classifying traditional Chinese grass-roofed dwellings into three types according to recognizable appearance features. Based on the YOLOv8 deep learning framework, a recognition model is constructed to recognize and spatially locate various grass-roofed dwellings from the image dataset on a county-level. Further, by conducting spatial overlap analysis with a variety of natural and socio-environmental factors on ArcGIS, their influences on the distribution pattern of traditional grass-roofed dwellings were examined. The study findings are as follows: (1) Traditional grass-roofed dwellings are concentrated on the southeast side of the Hu Line with different distribution patterns according to their types. (2) The natural environment influences the original construction and distribution of traditional grass-roofed dwellings in terms of the growth of grass resources and the ecological adaptability of grass material. (3) The development of economy, population, and urbanization pose challenges to the retention of grass-roofed dwellings. This research provides useful references for the precise preservation of various grass-roofed dwellings and introduced a novel approach for the classification of traditional buildings.

Keywords: grass materials; traditional dwellings; deep learning; distribution pattern; GIS; China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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