## 3 Materials and Methods
### Methodology
#### 3.3.1. Geons  
[PERSON] et al. [31] introduced the geons concept as aggregate units that have their own meaning and representativeness. Geons are, therefore, not the smallest possible geometric entities, and they are purpose-driven. Geons are closely related to object-based image analysis (OBIA) and object-based classification. The latter--with or without images as constituent parts--seems to be well suited for mimicking the subjectivity of visual interpretation in digital classification. [PERSON] and [PERSON] proposed an object-based classification on the basis that human vision can differentiate the thematic objects through groups of image pixels [51]. The object-based classification has been increasingly used for high-resolution classification [52]. Geons are well-defined as spatial units that are homogenous in terms of varying space and time phenomena under policy concern [31]. A geon is a type of region that is semi-automatically delineated using expert knowledge. The main aim of generating geons is to map policy-relevant spatial occurrences using an adaptive and expert-validated method to visualize and help to understand the spatial distribution to improve planning and evaluation measures for intervention [17]. Geons are scale-specific spatial objects with steadiness features, such as reduced inherent variance and gradients toward the outside through vector encoding [31,53]. A geons model works as a two-step process, where the first step is based on domain-specific expert knowledge, and the second is based on semi-automated regionalization through segmentation [54]. Object-based image analysis (OBIA) has various algorithms for segmentation. Multiresolution segmentation is a commonly used algorithm for the segmentation of images into image objects [52]. Multiresolution segmentation (MRS) creates homogenous image segments through scale representations. To minimize the loss of detail during the segmentation, aggregation of spectral information is carried out in a scale-adaptive manner. The MRS process starts aggregating from the pixel level and then aggregates into objects of varying shapes and sizes until the preset threshold is reached. The key issue with MRS is the selection of key parameters, especially the scale parameter (SP). To select the optimal scale parameter for the analysis the Estimation of Scale Parameter tool (ESP) was developed, which detects the optimal scale based on the local variance graph using a single layer [55]. This ESP tool was extended to ESP2, which is an automated method for the selection of the optimal scale parameter that provides three distinct scales using multiresolution segmentation. The geons model was entirely processed in eCognition (Trimble) software in combination with the ESP2 tool, which operates within the eCognition software environment and aims to obtain statistically significant scale parameters. The output of the ESP2 tool is three scale parameters. In this study, these parameters determine the SP of the input conditioning factors, and we used the finest scale parameter for the geons model. Lastly, all nine conditioning  
Figure 3: Workflow of the applied methodology. Note: LSM = landslide susceptibility mapping; ESP2 = estimation of scale parameters.  
factor layers were converted to GeoTIFF (georeferenced information embedded within a Tagged Image File Format) files, with normalized values ranging from 0-255 and 8-bit unsigned integer values for implementing geons in the eCognition software.