# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 1 Introduction  
Traditional Chinese settlements (TCSs) are the products of agricultural societies [1; 2]. Due to the limited productivity level, the advantages and disadvantages of natural conditions play a decisive role in agricultural production and human life [3; 4]. The rational use of natural resources and topographic advantages to avoid harm is the primary criterion for settlement site selection. Therefore, the ancient Chinese were adept at determining the place of residence based on geographies and natural resources, such as mountain shapes, riverbanks, forests, and pastures, to create relatively suitable production and living spaces [5; 6]. These patterns with regional environmental characteristics are regarded as settlement environmental patterns, which combine architecture and environment to realize the harmonious coexistence between humans and nature. Cognitive environmental patterns are useful for modern urban and rural planners to discover the traditional habitat wisdom embedded in TCSs, thereby guiding new village planning. However, most TCSs are located in remote areas with limited access, making it difficult to conduct manual surveys. Therefore, it is valuable to research the automatic recognition method of environmental patterns using the environmental data of TCSs. Traditionally, research on environmental patterns primarily relies on phenomenal summaries and inductive descriptions, focusing on the internal and external conditions, such as geographical distribution, topographic conditions, and material culture, to classifyTCSs in a qualitative manner [7; 8; 9]. To improve quantification, researchers have conducted quantitative comparative studies of TCSs using statistical learning methods [10; 11; 12; 13]. In recent years, statistical machine learning has achieved great success in many fields, such as agriculture [14], forestry [15], and climatology [16] by combining the experience, prior knowledge, and conceptual understanding of human experts, which inspires experts in traditional settlement studies to explore digital transformation. Consequently, scholars attempt to use the analytic hierarchy process to quantify the dominant elements of TCSs and then apply machine learning methods, such as SVM [17], clustering [17; 18], and random forest [19], to scientifically compare and classify them. Although these methods provide some ideas for environmental pattern recognition in TCSs, manually constructed features not only have difficulty describing complex human-land relationships, but also rely on features selected by the algorithm designer and environmental pattern recognition rules that depend on user-configured parameters. Setting these parameter values to obtain accurate recognition is difficult, especially when multiple parameters are involved. In recent years, high-resolution remote sensing images have enabled the rapid acquisition of spatial structures, morphological characteristics, and environmental information related to settlements [20; 21; 22], which enables the remote sensing image scene classification methods to provide a new way to automatically recognize the TCSs. Among them, relying on the deep neural network can automatically attain global features from the input data and treat the scene classification task as an end-to-end problem [23; 24; 25]. The features learned by deep learning methods are often better and simpler than those designed manually. However, the drawback of deep learning is also obvious. High classification performance presupposes sufficient supervised learning samples, which is often difficult. In most cases, we only have a small number of available TCS samples, and the classic classification networks [26; 27; 28; 29] perform poorly when they encounter few samples. In addition, TCSs are formed spontaneously during the long-term interaction between humans and nature and have distinctive regional characteristics, which makes it difficult for trained models to effectively recognize the patterns of TCSs in unknown areas.