# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 2 Related Work
### Classification Study of TCSs  
Traditionally, classification research on TCSs is carried out manually [7; 8; 9], which is often limited in scope and time-consuming. To overcome this problem, researchers have attempted to conduct comparative studies of TCSs using statistical learning methods. For example, refs. [10; 11] applied Space syntax to classify settlements by topological features, such as spatial depth, connectivity, and integration. Additionally, refs. [12; 13] focused on an abstracted plane to quantify the shape, structure, and order of the settlement plane in order to establish a two-dimensional morphological analysis model for classification purposes. With the rise in digital research on TCSs, machine learning techniques have also been applied. For instance, [PERSON] et al. [17] used Support Vector Machines (SVM) to distinguish between new-style and old-style rural settlements based on a landscape analysis approach. [PERSON] et al. [18] employed an Analytic Hierarchy Process (AHP) to construct a  
Figure 1: The workflow of the proposed method, which has four major components: (**a**) dataset construction; (**b**) benchmarking the dataset; (**c**) proposing a new method; (**d**) model training and evaluation.  
morphological quantification index in three dimensions for mountain settlements and then apply clustering methods to recognize the spatial morphology of Miao settlements in Qiandongnan. Subsequently, [PERSON] et al. [19] studied the environmental factors of 4000 TCSs, constructing a quantitative index system of settlement environmental factors by topography, meteorology, and natural resources. Then, clustering algorithms were applied to recognize the geospatial patterns of these settlements.  
However, the classification of TCS environmental patterns is still an unresolved issue due to the lack of clear rules, which is reflected in the fact that researchers usually limit their research to a certain area and formulate corresponding rules according to the characteristics of that area. These rules force researchers to manually extract a large number of features based on the study perspective, which is a tedious process and lacks generalizability. Although deep learning methods have powerful feature extraction and characterization capabilities that can replace the process of feature construction to some extent, deep learning methods rely on large amounts of labeled data, which is difficult for a limited number of TCSs. In addition, the inexplicability and security risks [31] of deep learning are criticized by researchers in the field of urban and rural planning, which restrict the development of deep learning techniques in the field of environmental pattern research in TCSs. To address these issues, we investigated environmental factors and found that elements in natural environments such as mountains, rivers, farmlands, forests, and vegetation regions play dominant roles in environmental patterns [17; 18; 19; 32; 33; 34]. Therefore, our goal is to segment the remote sensing images of TCSs into settlement environment maps composed of dominant elements, thus incorporating expert knowledge in the field of urban and rural planning. On this basis, the similarity between TCSs is used to achieve automatic recognition, enabling the use of interpretable artificial intelligence (XAI) [35] methods to drive digital transformation in the field of environmental pattern research.