The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-M-1-2021
28 Aug 2021
 | 28 Aug 2021


M. Zhang, Z. Li, and X. Wu

Keywords: Semantic Segmentation, Convolutional Neural Network (CNN), Remote Sensing Image, Traditional Chinese village, Spatial Form

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.