## 1 Introduction  
Natural hazards occur frequently in Taiwan. Among them, landslides can be triggered by heavy rainfall events, especially in mountainous areas. The precipitous terrain, complicated geology, and dense population may increase vulnerability, causing a serious loss of life, property damage, and economic loss. Furthermore, these events can affect water resource supply and cause other livelihood problems. Therefore, landslide analysis and assessment have become critical in natural hazard and disaster mitigation, prevention, and reconstruction in Taiwan. A growing number of studies have investigated landslide-related topics on different scales, such as slope stability analysis for a specific slope site [1; 2]; regional landslide detection and mapping [3; 4; 5; 6; 7]; characterization of the relationship between landslides and environmental or triggering factors [8; 9; 10; 11; 12; 13]; susceptibility, hazard, risk assessment, and management [14; 15; 16; 17; 18; 19]; and modeling or estimating physical, environmental, and rainfall-based parameters [20; 21; 22; 23; 24]. Landslide risk assessment and management are crucial, systematic, and extensive frameworks in the related works. [PERSON] et al. [14] divided this topic into several parts. In this framework, generating landslide inventory (inventory map and database are used synonymously in this study) is essential to connect the following assignments. A basic landslide inventory should record the landslide's location, date (event-based) or period (multitemporal), and types of movement [25]. The definition, assumptions, requirements, production processes, and statistical properties of landslide inventories have been discussed in detail by [PERSON] et al. [25], [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al. [28]. Furthermore, [PERSON] et al. [29] and [PERSON] et al. [11] have assessed the quality and completeness of landslide inventory maps by comparing two maps of the same area. Numerous studies have indicated that the continuing improvements in remote sensing and geographic information systems (GISs) have led to cooperation with data mining and machine learning algorithms to produce a regional landslide inventory. In particular, integrating GIS-based models with geo-spatial data [18; 30] and generating event-based landslide inventory [11; 25; 31] have garnered interest. Three common features of typical natural terrain landslides have been observed from a geotechnical perspective. These features were the landslide source, trail, and deposition fan [32]. The surface of the rupture, comprising the main scrap and the scrap floor, is defined as the source area. A landslide trail may also occur predominantly as a result of the transport of the landslide mass. The majority of the landslide mass is deposited (i.e., deposition fan). The term runout is generally used to indicate the landslide trail and deposition fan [33]. Landslide detection performed using automatic and semiautomatic methods with remotely sensed images usually includes runout areas unless these results have been excluded manually through a detailed comparison with stereo aerial photos and other auxiliary data. The areas detected using remotely sensed landslide detection can be referred to as \"landslide affected\" areas. Mixing landslide source areas with runout regions may reduce the reliability of a landslide analysis [34], such as susceptibility and hazard assessments. These - runout areas should be separate from landslide source areas because they have different mechanisms. More precisely, producing landslide inventory requires further processing after the remote sensing of landslide detections. Data mining and machine learning-based algorithms have been used increasingly for landslide modeling, especially in landslide susceptibility assessments, such as decision trees, deep learning systems, evolution-based algorithms, fuzzy theory, neural networks, random forests (RF), and support vector machines (SVM) [35; 36; 37; 38; 39; 40].