## 3 Results
### Classification Model  
The results of the three independent analyses--i.e., susceptibility algorithm, DInSAR data analyses and damage survey--were combined for the 3220 \(TZU_{road}\) identified in Vaglio Basilicata and for the 1373 \(TZU_{road}\) identified in Trivigno, following the procedure described in Section 2.1.3. Figure 7 shows two excerpts of the maps that are produced by the procedure in two small portions of the study area in Vaglio Basilicata (a) and Trivigno (b), as well as the correlation matrices obtained for the classification carried out over the entire territory of the two study areas. The matrices show that about half of the \(TZU_{road}\) are classified as being at risk in both study areas (1726 at Vaglio Basilicata and 689 at Trivigno), mainly as a result of the significant presence of mapped landslides interacting with the road network in these two municipalities. In particular, the analyses indicate that 966 \(TZU_{road}\) at Vaglio Basilicata (56% of the \(TZU_{road}\) classified at risk) and 504 at Trivigno (73% of the  
Figure 6: Maps of damaged road stretches with severity levels resulting from the damage classification using Google Street View imagery dated August 2021 for Vaglio Basilicata (**a**) and March 2021 for Trivigno (**b**). Percentages of damaged road distinguished according to the four (D0–D3) damage severity levels are also reported (c). _TZU\({}_{road}\)_ classified at risk) are classified at high or medium risk (i.e., presence of a mapped landslide and two indicators positive). Regarding _TZU\({}_{road}\)_ falling outside the mapped landslide bodies, and thus classified with attention levels, 1298 _TZU\({}_{road}\)_ out of 1494 in Vaglio Basilicata (87%) and 541 _TZU\({}_{road}\)_ out of 684 in Trivigno (79%) are classified as requiring high or medium attention. These results should be considered as associated with possible effects on the road network caused by the spreading of existing landslides, or by the potential activity of landslides not mapped in the inventory. Only 22 and 8 _TZU\({}_{road}\)_ are classified as being at very low risk and requiring very low attention, respectively, for the studied road network in Vaglio Basilicata, and only 4 and 8 _TZU\({}_{road}\)_, respectively, in Trivigno. This can be justified by the conservative choice made in the implemented classification matrix, which considers a _TZU\({}_{road}\)_ not covered by the DInSAR data as \"moving\" and a _TZU\({}_{road}\)_ not covered by the damage survey as \"damaged\". For classification purposes, the road networks surveyed in the two municipalities are divided in stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the related _TZU\({}_{road}\)_.  
Figure 7: Excerpts of maps produced by the procedure in two small portions of the study area in Vaglio Basilicata (**a**) and Trivigno (**b**). The tables below report the correlation matrices obtained for the classification carried out over the entire territory of the two study areas. The aggregation is carried out by merging the \(TZU_{road}\) indicating the same levels of risk (or attention) along the road network. By applying this procedure, the road networks in Vaglio Basilicata and Trivigno are divided into 213 and 134 stretches, respectively (Figure 8a,b). For those sections where the indication provided by the correlation matrices is not fully univocal because the \(TZU_{road}\) are characterized by several different levels of risk or attention (an example is the road track in the upper left part of Figure 8b), the highest level is considered. Table 7 summarizes the results shown in Figure 8, reporting the total lengths of each class of road stretch.