## 1 Introduction  
Natural hazards worldwide are increasingly causing the loss of lives and resulting in increasing economic losses [1]. Landslides are one of the most common and prominently occurring natural hazards in mountainous regions, impacting human lives and infrastructure and causing economic loss [2]. A landslide is defined as the gravitational movement of a mass of rock or debris down a slope [3]. In general, landslides are mass movements that include rock falls, mudslides, and debris flows. Classifications of landslides are usually based on the material, such as rock, debris, earth, or mud; and the movement type, such as fall, topple, avalanche, slide, flow, or spread. Triggering factors for landslides can be natural processes, such as heavy and prolonged precipitation, earthquakes, rapid snowmelt, and permafrost thawing; or human-made factors, such as the construction of roads, buildings, irrigation, or combinations of natural and human-made triggers [4]. The occurrence of landslides is likely to increase in the future due to the impact of human activities, population growth, infrastructure development, and mistreatment of natural resources. Landslides can, in principle, be considered natural phenomena, but they are often triggered by the impact of human undertakings [5]. With the increasing impacts of landslides worldwide, a substantial increase in scientific research on landslides can also be observed [6]. Especially Europe witnessed a substantial increase in mortalities and economic losses from natural hazards, which were mainly landslides. These landslides occurred as a result of physical, geological, meteorological, morphological, and anthropogenic factors [7]. To comprehend and study the influence of landslides on human and economic losses, it is essential to study a region's susceptibility to landslides. Landslide susceptibility is the potential impact of a particular type of landslide on a specific area in the future [8]. Landslide susceptibility mapping (LSM) is the spatially explicit quantification of the probability of the occurrence of landslides in the future based on the effects of conditioning factors in the given area [9]. A susceptibility map identifies areas within the study region that are more or less prone to landslides in the future based on relatively higher or lower probabilities or classes of probabilities [10]. The LSM is decisive in the management of landslides, and various methods have been used to analyze landslide susceptibility [11; 12]. LSM aims to quantify, map, and ultimately understand the spatial distribution of landslide occurrences in the future [13]. Although susceptibilities to certain other types of natural hazards may be easier or more exact to model and map (e.g., floods), landslide susceptibilities are an effective and widespread planning tool [14]. Many previous studies target the landslide susceptibility at a regional level or for a single watershed, while research at a national scale is rare [15]. The advancement in technologies such as geographic information systems (GIS) and remote sensing (RS) have considerably assisted in enhancing planning and mitigating future natural catastrophic events [16]. There are various methods for extracting information from images through GIS and RS. The object-based extraction of information for landslide susceptibility has been predominant in recent years [17]. Most of the LSM has been carried out using either a pixel-based, data-driven approach or an expert-based approach. Various methods are being used for LSM, such as the data-driven frequency ratio (FR) [18; 19; 20], expert-based analytical hierarchical process (AHP) [21; 22], evidence belief function (EBF) [23], machine learning models of support vector machines (SVM) [24], random forest (RF) [25], and logistic regression (LR) [26; 27]. During the past two decades, object-based image analysis (OBIA) has developed as an extension of Geographic Information Science (GIScience) and is used to analyze and process very high-resolution satellite imagery [28].