# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 3 Dataset Construction
### Data Collection  
TCSs are formed spontaneously over the course of long-term interactions between humans and nature, and they demonstrate distinct geographical characteristics [50]. According to the Ministry of Housing and Urban-Rural Development of China, there are currently 6821 recorded TCSs throughout the nation. Information on them has been introduced on the Traditional Chinese Settlements Digital Museum ([[http://www.dmctv.cn/directories.aspx](http://www.dmctv.cn/directories.aspx)]([http://www.dmctv.cn/directories.aspx](http://www.dmctv.cn/directories.aspx))) (accessed on 20 February 2023). For the purpose of automatic recognition of environmental patterns using deep learning techniques, data from three different areas with a variety of landforms have been collected. The first area is the Qiangdongnan Miao and Dong Autonomous Prefecture (Qiangdongnan, Figure 2b, 107\({}^{\circ}\)17\({}^{\prime}\) E-109\({}^{\circ}\)35\({}^{\prime}\) E, 25\({}^{\circ}\)19\({}^{\prime}\) N-27\({}^{\circ}\)31\({}^{\prime}\) N) on the Yunnan-Kweichow Plateau, with an area of 30,282 km\({}^{2}\). This region is home to many well-preserved TCSs. The second area is Shaanxi Province (Figure 2c, 105\({}^{\circ}\)29\({}^{\prime}\) E-111\({}^{\circ}\)15\({}^{\prime}\) E, 31\({}^{\circ}\)42\({}^{\prime}\) N-39\({}^{\circ}\)35\({}^{\prime}\) N), situated in the Chinese hinterland. It encompasses an area of 205,624 km\({}^{2}\) and is renowned for its diverse range of landforms, including loes tablelands, mountains, plains, and basins. Lastly, the Anhui Province (Figure 2d, 114\({}^{\circ}\)54\({}^{\prime}\) E-119\({}^{\circ}\)37\({}^{\prime}\) E, 29\({}^{\circ}\)41\({}^{\prime}\) N-34\({}^{\circ}\)38\({}^{\prime}\) N), located in the Yangtze River Delta region of East China, offers an area of 140,100 km\({}^{2}\), consisting of various plains, hills, rivers, and lakes. Next, we investigate five environmental patterns of TCSs in the above areas, including river valley, foothill, riverine, hillside, and plain. Among them, the settlements with river valley patterns usually occupy a valley, are surrounded by mountains, and are often located near rivers. The settlements with foothill patterns tend to be located on flat slopes, along ridges, or at the base of hills. Riverine settlements are typically situated near rivers or dispersed along these waterways in a band. Hillside patterns of settlements often exist on the sides of mountains, characterized by a steep drop-off between peaks and deep ravines. The settlement planning boundary is broken and follows the terrain in multiple directions. Finally, plain settlements are usually situated on flat plains in patches, and they often display higher densities and structural forms. To acquire the settlements and their environmental data, Pleiades satellite images from Airbus Defence and Space (with a spatial resolution of 0.5 m) are selected. The remote sensing images of five environmental pattern TCSs in the mentioned areas are cropped to 2560 \(\times\) 2560 pixels, with the settlement being located at the center. Additionally, the corresponding DEM data with a 10 m spatial resolution are also considered, based on the latitude and longitude. A sample of 648 TCSs is collected, consisting of a remote sensing image paired with one piece of DEM data, as shown in Table 1. The number of TCSs of each type varied greatly, and even the same type had significant regional distinctions across different regions. An example of this difference could be seen in the hillside pattern settlements displayed in Figure 3.  
Figure 2: Location of the three researched areas. (**a**) The provincial boundaries of China; (**b**) the location of the Qiandongnan Miao and Dong Autonomous Prefecture; (**c**) the location of the Shaanxi Province; (**d**) the location of the Anhui Province.