## 4 Results  
### Geons  
The resulting LSMs were generated using the geons model to identify the regions susceptible to landslides in Austria. Weights for each conditioning factor were based on expert knowledge and were used for multiresolution segmentation. The resulting maps were classified based on the quantile classification technique, which is a commonly used classification technique for natural hazard susceptibility mapping. The results were classified into five categories: very low, low, moderate, high,and very high. Figure 4 shows the LSM map derived from geons considering the optimal SP of 1500, and Figure 5 shows the LSM from geons with regions.  
For a better understanding of how each conditioning factor influenced the final LSM derived from the geons model, we show the susceptibility classes of the areas categorized into five classes.  
Figure 4: The LSM derived from geons using the optimal scale parameter (SP) of 1500 and the validation landslide locations.  
Figure 5: The LSM derived from geons using the optimal SP of 1500, showing the regions and the validation landslide locations.  
The advantage of the geons model is that it shows how each region (geon) has been characterized, and the impact of each conditioning factor on the resulting LSM. We selected four random locations from the derived LSM and visualized the impact of each conditioning factor in these random locations.  
Figure 6 shows the four random locations chosen to understand the impact of each conditioning factor on the final landslide susceptibility map using the geons model.  
Figure 7 depicts the impact of each conditioning factor at location one. At location one, the impact of the rainfall conditioning factor was the highest, followed by elevation and distance to drainage, whereas the distance to roads had the least impact at that location, followed by lithology, land cover, and aspect. In the final susceptibility map, location 1 is classified as having a very high susceptibility to landslides.  
Figure 8 depicts the impact of each conditioning factor at the second location. At location two, the impact of the distance to the faults conditioning factor was highest, followed by rainfall, whereas  
Figure 6: Four locations within the study area were chosen for the impact assessment of each conditioning factor on the final susceptibility map using geons.  
Figure 7: Impact of each conditioning factor at location \(1\) within the study area.  
elevation and distance to drainage had no impact at all on the final susceptibility mapping. Factors such as slope and distance to roads had the least or minimal impact on this location. In the final susceptibility map, location 2 falls into the class of low susceptibility.  
Figure 9 depicts the impact of each conditioning factor at the third location. Elevation, slope, and rainfall had no impact at all at this location compared to the distance to drainage, which had minimal impact on the final susceptibility map. Distance to faults had a significant impact, followed by land cover, aspect, and distance to roads for the final susceptibility map. In the final susceptibility map, location 3 falls into the very low susceptibility class.  
Figure 10 depicts the impact of each conditioning factor at the fourth location. At location four, distance to roads, distance to faults, and distance to drainage had no impact at all on the final susceptibility map. Slope and lithology had minimal impact on the final susceptibility map, whereas rainfall and elevation had major impacts, followed by land cover and aspect factors. In the final susceptibility map, location 4 falls into the moderate susceptibility category.  
Figure 8: Impact of each conditioning factor at location 2 within the study area.  
Figure 9: Impact of each conditioning factor at location 3 within the study area.