## 1 Introduction
OBIA builds on image analysis, which is an older concept similar to image segmentation and image classification. The object-based classification presents a viable alternative to the pixel-based approach [29]. The image segmentation process is a crucial prerequisite for classification [30] and further analysis in the so-called geons approach. Geons are spatial units that are \"homogenous in terms of varying space and time phenomena under policy concern\" [31]. This geons model incorporates expert knowledge and semi-automatically delineates regions. The method will be explained in greater detail in Section 3.3.1. The resulting landslide susceptibility maps are highly dependent on the input data, namely the landslide inventory and the influencing conditioning factors relevant to the study area. The study of LSM at a national scale has been carried out for the first time using an object-based geons model along with nine conditioning factors relevant to the study area. The overall aim is to use geons for the LSM, utilize the DST to optimize the resulting LSMs from multi scales, and reduce the associated uncertainty. The image segmentation approach can affect the accuracy of the classification and has a considerable influence on the resulting LSMs. An incorrect segmentation usually results in over-segmentation and under segmentation errors [32]. Thus, finding the optimal parameters for the segmentation process plays an essential role in LSM using geons. The optimum scale parameter (SP) must be considered for the process of finding the appropriate segments for the susceptibility analyses [33]. Although there is never an ideal solution to the segmentation process, this process can lead to better results when using the optimal SP [34]. Although some landslide conditioning factors are prepared from RS data with a similar spatial resolution (e.g., the slope and elevation), there are some factors with different resolutions (e.g., rainfall and lithology) that are generated from different sources. Thus, all the LSM is done within a multiresolution and multiscale environment [35]. Therefore, an optimal scale-out of multiple scales can be found for the LSM with less internal heterogeneity compared to the adjacent areas [36]. The optimal SP for segmenting our data was obtained by applying the estimation of scale parameters (ESP2) tool [33]. Multiscale were chosen using interval values based on the optimal SP. The resulting LSMs from multiscale segmentation and geons were then fused using Dempster-Shafer theory (DST) to show the higher performance of susceptibility map generation in a multiscale environment. The DST, which is also known as evidence theory, is principally based on representing independent sections of evidence by completely monotone capacities (also known as belief functions), and pooling them by means of a common operator called Dempster's rule of combination [37]. This probability method has been used to optimize the results of various natural hazard susceptibility mapping projects [38; 39; 40]. In this study, we use the OBIA-based aggregation approach of \"geons\" for the LSM of Austria. In Austria, landslides are a significant natural hazard in alpine and mountainous areas. We investigate the conditioning factors, such as lithology, geology, land cover, and geomorphology (primarily slope angle and aspect), and produce LSM for the whole territory. This is of high importance because recent landslide events in Austria have caused severe mortalities, as well as high economic losses.