Abstract
Abstract. Rapid investigation and quantitative analysis are crucial for heritage conservation and renewal design. As an important category of architectural heritage - traditional settlements - with their large number and complex spatial characteristics, their spatial character patterns are an important support to assist settlement conservation and renewal design. However, the current means of analysis often requires manual data collection, secondary mapping of the collected data, extraction of individual elemental patterns and village boundaries. Then settlement boundary form, settlement density will be calculated by mathematical methods. The above methods are inefficient and prone to manual mapping errors, making it difficult to quantify and analyze a large number of traditional villages in a short period of time. Semantic segmentation is a computer vision technique for quickly segmenting different objects. Based on the collected remote sensing data of traditional villages, this paper established a dataset of semantic segmentation of spatial features of traditional settlements, segmenting village buildings, water systems, roads and plants. Using Transfer learning, data augmentation and other methods, a model was trained that can automatically segment elements of the villages. From the national traditional villages that have been announced so far, 60 traditional villages from different regions in the north and south were selected for analysis. The experiments show that the model established in this paper has an accuracy rate of above 86% in segmenting elements of villages, can effectively identify the location of different elements in remote sensing images, effectively improves the quantification rate of spatial features of settlements and saves the cost of mapping and data transcription. The results of the spatial characteristics of the 60 villages studied in this paper can also provide some theoretical basis and inspiration for the study, conservation, design and transformation of traditional villages.
Highlights
The traditional village is a representative of Chinese residential architecture, and is a settlement space with basic forms such as cluster, band and radiation, formed by people gathering and settling in accordance with the natural environment and social culture (Hao and Zao, 2019)
In the study of quantification of traditional village features, commonly the CAD plans of all buildings in the village are drawn manually first (Yun, 2009), buildings are abstracted as points, and the mathematical model is established by computer and the concept of village space is analyzed, or the village morphology is quantified through fractal dimension (Weiguo and Mengjia, 2021), the quantification of the village interior spatial characteristics of the village can be realized
This paper introduces remote sensing images and deep learning methods in the spatial quantification of traditional villages to achieve semantic segmentation of buildings; and combines computer vision related algorithms to achieve automatic quantitative extraction of spatial features of traditional villages based on the results of semantic segmentation
Summary
The traditional village is a representative of Chinese residential architecture, and is a settlement space with basic forms such as cluster, band and radiation, formed by people gathering and settling in accordance with the natural environment and social culture (Hao and Zao, 2019). The number of national-level traditional villages certified in China in 2020 has reached 6,819, making it one of the largest living farming settlements in the world and an important vehicle for the transmission of Chinese civilization (Zhe et al, 2019). With the development of photogrammetry and point cloud technology, automatic extraction of point cloud data based on village features has been realized (Zhe et al, 2019), it still requires manual operation of UAVs to collect images for village field research and obtain point cloud data through photogrammetry. The current approaches to spatial quantification of traditional villages to some extent require manual collection, data extraction, or remapping of image data. The extraction of spatial features of all national traditional villages by the current methods is labor-intensive. It is difficult to achieve a spatial overview of all villages in a short period of time
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