# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 2 Study Area and Data
### Spatial Datasets  
In applying the data-driven methodology based on the established landslide (of debris flow in this study area) locations, accurate data is essential to build an appropriate database. In this study, data on the location of landslides was acquired based on aerial photographs. Additionally, 17 variables were selected as landslide-related variables; the variables were selected by referring to previous studies on various aspects of landslides, as follows: nine topographical variables (slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), slope length factor (SLF), standardized height, valley depth, and downslope distance gradient (DDG)), four forest variables (timber type, timber diameter, timber density, and timber age) and four soil variables (soil depth, soil drainage, soil topography, and soil texture). The variables were selected as correlating between the landslides, and the variables have been investigated through the frequency ratio model in previous research [46; 52; 53]. Landslides in the Mt. Umyeon area received a lot of attention because damage occurred in areas where human activity was high, including major roads and residential areas in Seoul. Accordingly, a large-scale investigation into the cause and damage was conducted, so that a large amount of data, such as aerial photographs and field survey data, have been accumulated. Aerial photographs provide accurate status on the condition of the surface, with effectively captured complex urban structures. Also, despite the high cost of aerial photography, continuous monitoring using images is cost-effective for dense urban areas. The data on the location of landslides were selected by comparing the aerial photographs of Mt. Umyeon in 2010 before the occurrence and immediately after the occurrence in 2011 (Figure 2). Starting with the loss of the top slope, a debris flow occurred in which rocks and waste wood contained in the weathering and debris layers were lost together [54]. Accordingly, the landslide occurrence point was extracted based on the landslide occurrence starting point in this study, as in previous studies [27; 31; 55]. The traces of landslides in Mt. Umyeon remain for several years after the occurrence of debris flow, so it was easy to obtain through aerial photographic analysis. In addition,  
Figure 1: The study area of Mt. Umyeon. overlay and comparative analysis of 1:5000 digital topographic maps were performed, and 103 starting points of the landslide from landslide inventory were finally determined by comparing it with field survey data [56] conducted by the Seoul Metropolitan Government. Slopes have an important effect on the occurrence of landslides, as they affect water flow and soil characteristics [46; 57]. Slope is a main landslide influencing factor that has the greatest impact on landslides, in that gentle slopes tend to have a lower frequency of landslides [55; 58]. Therefore, the input layer for landslide susceptibility analysis was constructed by focusing on topographic and geomorphologic variables among the variables that influence landslides (Figure 3). The topographic variables were calculated based on the acquisition of a digital topographic map (1:5000) generated by the National Geographic Information Institute (NGII); the slope was calculated using ArcGIS software (ESRI, Redlands, LA, United States) after geometric correction (Figure 3a), and the following additional topographic and geomorphologic variables were calculated using SAGA (Automated Geoscientific Analyses) GIS [59]. Slope aspect was classified into flat and north, northeast, east, southeast, south, southwest, and northwest, with 25 degree intervals (Figure 3b). The curvature variable represents the morphological characteristics of the research area by expressing the upwardly convex surface as a positive value, and the upwardly concave surface as a negative value.