# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 1 Introduction
To address these limitations, we investigate the application of deep learning techniques in the environmental pattern recognition task of TCSs with the workflow shown in Figure 1. First, we download remote sensing images and digital elevation model (DEM) data of TCSs from three different topographic areas in China. Among them, DEM is a three-dimensional array-based digital elevation model that represents the surface morphology of the Earth by quantifying the mean altitude of the sampled areas through discretized elevation data. It can effectively supplement missing elevation information in remote sensing images. With some preprocessing work, a new TCS dataset by region was built, which includes 648 TCSs with five environmental patterns, such as river valley, foothill, and riverine. Second, three representative convolutional neural networks (CNNs), AlexNet [26], ResNet [27], and DenseNet [28], are used to benchmark the new dataset. The three CNNs achieved near-perfect performance on the training set but performed poorly on the test set. Because the samples in the training and test sets are from different areas, this leads to serious overfitting problems in the CNNs under the conditions of sparse samples and regional differences. To solve this problem, we propose a new deep learning method by introducing pre-segmentation and metric-based meta-learning techniques to CNNs. Specifically, a semantic segmentation model is used to segment the input data of remote sensing images and DEM data into settlement environment maps composed of seven elements, including mountains, water, forests, and farmland. Subsequently, the environmental pattern recognition of TCSs containing unknown areas is regarded as a few-shot classification problem [29; 30], where the areas with a large number of samples will be used as the base dataset to train the model, and the areas containing only a small number of samples will be used as the novel dataset, enabling few-shot recognition using the similarity between the support and query samples. Finally, we perform model training, evaluation, and comparative evaluations to demonstrate the effectiveness of the proposed method. This work provides a new way to research and recognize the environmental patterns of TCSs. The primary contributions are as follows:  
* The environmental pattern recognition of TCSs is formalized as an image processing task, addressed by a deep learning model trained with remote sensing images and DEM data. More specifically, these two types of data are combined into four-channel inputs to extract environmental features and perform automatic recognition using CNNs. * A semantic segmentation model is used to segment the input data into settlement environment maps consisting of dominant elements, which helps to fuse expert prior knowledge to reduce the influence of noise in remote sensing images and improve interpretability. * A metric-based meta-learning method incorporating pre-segmentation strategies is proposed to achieve the few-shot recognition of environmental patterns under conditions of sample scarcity and geographical differences in TCSs. The rest of this paper is organized as follows. In Section 2, we review the related work on TCS classification research and introduce our idea. Section 3 describes the construction of the new dataset. Section 4 introduces the proposed method. Section 5 contains experiments and analysis, and the last section concludes.