## 4 Application of the Trained NN and Results for the Central Italy Case Study  
In order to evaluate the performance of NNs in terms of retrieval accuracy and generalization capability, they were applied to the Amatrice earthquake. The goal is also to evaluate NN capability for modelling the building collapse ratio. The satellite dataset is made up of two Sentinel-2 optical images (1 pre- and 2 post-seismic), three COSMO-Sky SAR images (2 pre- and 1 post-seismic), and a building footprint layer extracted by the Open Street Map service (see Table 2 for EO data characteristics). Both optical and SAR data have a 10 m spatial resolution. The Amatrice footprint layer extracted from OSM, which is at a single building scale, was modified to obtain polygons surrounding more than one building. In this way, there are more pixels associated to each polygon leading a better estimation of change features over the polygon itself. The resulting layer consists of 112 polygons. In the Figure 6, histograms for the CR obtained by the NN approach, Exp 1 (a) and Exp 2 (b), are depicted. The histogram comparison shows that Exp 1 estimates most of polygons with a CR less than 0.1 (92 polygons, 82% of total), which means no damage. Five polygons present a CR between 0.1 and 0.2 and just one between 0.2 and 0.3. Only about 13% of polygons have a CR higher than 0.3, which spans from medium damaging to total collapsing. Specifically, three polygons with CR between 0.3 and 0.4, four between 0.4 and 0.6, six between 0.6 and 0.8, and only one higher than 0.8, indicating total collapse. The location of polygons is well shown in Figure 7a, which describes the damage CR maps obtained analyzing post 24 August 2016. The map indicates that the severe damage is mainly retrieved in the northeast part of the Amatrice (red blocks), although information from Civil Protection confirms that collapses involved the whole historical center. This is confirmed by looking at an optical very high resolution (VHR) image (Figure 8) acquired after the Amatrice seismic event. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datatype** & **Satellite** & **Acquisition** \\ \hline Optical & Sentinel-2 & 2016/08/14 \\ Optical & Sentinel-2 & 2016/09/04 \\ SAR & COSMO-Sky & 2016/07/19 \\ SAR & COSMO-Sky & 2016/08/20 \\ SAR & COSMO-Sky & 2016/08/28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: EO data list for NN application. Figure 6: NN Collapse Ratio estimated after the M6.0 earthquake on 24 August 2016. (a) NN approach using optical inputs and (b) optical and SAR. The fusion of optical and SAR features significantly changes this distribution (Figure 7b), where occurrences of CR less than 0.1 (blue blocks) drastically decrease with respect to the Exp 1 (19 polygons, only 17% of total). About 54% of polygons show a CR between 0.1 and 0.2 (grey blocks), indicating light damage. Looking at map in Figure 7b, these light damages lie partially in the historical center, but most of them are located in the southeast part of the city. Considering only the historical center and the comparison with the VHR image (Figure 8), we suppose that Exp 2, involving SAR features, is more sensitive to light/medium damage level. Indeed, 14 polygons present a CR between 0.2 and 0.3, which indicate a medium damage level. We also notice that these polygons are located in the southeast part of the historical city center, an area involving severe damage as confirmed by the VHR image depicted in Figure 8. Considering CR higher than 0.3, polygons retrieved by Exp 2 increase by about 27% (19 respects of previous 14 of Exp 1). The NN employed for the data fusion approach (Figure 7b), better identifies the severely damaged areas in the historical center, also where Exp 1 underestimated the collapses. At the same time, it seems to overestimate the damage in other areas, especially outside of the historical center. Indeed, Exp 2 indicates zones characterized by building collapses also in the southeast and partially in the northwest (Figure 7b).