## 3 Results
### Dense Image Matching (DIM)
#### 3.2.3 Analysis of Roof Areas  
Several examples of unaltered building roofs were selected. First, a profile crossing a high building is considered. Figure 13a reports the different profiles for each point cloud. The dataset scanned with a consumer-grade scanner and processed in Metashape presents a height offset of about 1 m with respect to the other data. It is also worth noting the transition between the ground and the roof, with a significant smoothing effect, except for Inpho, on the photogrammetric dataset. The shaded side of the building proves that both DIM methods struggle to reconstruct the ground near the building. Other building roof areas were instead compared with the available LiDAR reference using a cloud-to-cloud distance within CloudCompare [50]. Visual examples are reported in Figures 14-16, whereas statistics (total number of points, point density in the chosen area, average deviation of points from the reference cloud, and standard deviation) are presented in Tables 7-9. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Image Matching Scenario**} & \multicolumn{3}{c}{**RMSE [m]**} \\ \cline{2-4}  & **Profile 1** & **Profile 2** & **Profile 3** \\ \hline Metashape 2\(\times\) GSD—photogrammetric & 0.187 & 0.075 & 0.119 \\ Metashape 4\(\times\) GSD—photogrammetric & 0.169 & 0.066 & 0.100 \\ \hline Metashape 2\(\times\) GSD—consumer-grade & 0.327 & 0.365 & 0.538 \\ Metashape 4\(\times\) GSD—consumer-grade & 0.298 & 0.287 & 0.542 \\ \hline Inpho 1\(\times\) GSD—photogrammetric & 0.385 & 0.158 & 0.186 \\ Inpho 3\(\times\) GSD—photogrammetric & 0.358 & 0.151 & 0.154 \\ \hline Inpho 1\(\times\) GSD—consumer-grade & 0.330 & 0.254 & 0.200 \\ Inpho 3\(\times\) GSD—consumer-grade & 0.378 & 0.231 & 0.294 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSE results for the three selected profiles in the image-based DSMs. Figure 12: Analysis based on DSM profile (**a**) no. 3 (parking lot near a building): orthophoto from 1986 (**b**) and 2012 (**c**). Figure 14: The roof area of the first building (red polygon, ca 10 \(\times\) 30 m) for the cloud-to-cloud comparison: views in the 2012 orthophoto (**a**) and in the 1986 dataset (**b**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Matching Scenario** & \begin{tabular}{c} **Total No.** \\ **of Points** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **[pt/m\({}^{2}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Avg.** \\ **Dist. [m]** \\ \end{tabular} &
\begin{tabular}{c} **Stand.** \\ **Dev. [m]** \\ \end{tabular} \\ \hline Metashape 2\(\times\) GSD—photogrammetric & 34,092 & 113.6 & \(-\)0.121 & 0.108 \\ Metashape 4\(\times\) GSD—photogrammetric & 12,739 & 42.3 & \(-\)0.132 & 0.112 \\ \hline Metashape 2\(\times\) GSD—consumer-grade & 10,121 & 33.5 & \(-\)0.170 & 0.127 \\ Metashape 4\(\times\) GSD—consumer-grade & 3952 & 13.0 & \(-\)0.088 & 0.129 \\ \hline Inpho 1\(\times\) GSD—photogrammetric & 144,267 & 474.6 & 0.054 & 0.123 \\ Inpho 3\(\times\) GSD—photogrammetric & 24,843 & 81.5 & 0.057 & 0.123 \\ \hline Inpho 1\(\times\) GSD—consumer-grade & 145,365 & 475.0 & \(-\)0.289 & 0.128 \\ Inpho 3\(\times\) GSD—consumer-grade & 25,597 & 83.4 & \(-\)0.241 & 0.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics for the cloud-to-cloud analysis of roof 1 (Figure 14). Figure 13: Profile line (**a**) crossing the building: orthophoto overview from 1986 (**b**) and 2012 (**c**). In all examples, the lowest mean values are obtained in the photogrammetric dataset, whereas in the consumer-grade dataset, for both DIM methods, errors are much larger. After analyzing the standard deviation values, we note that the value is similar for each set, meaning that the data are internally consistent in the same range (comparable noise). These examples of building roofs show how the photogrammetric dataset provides significantly better results with up to 1-2 GSD size accuracy and contains fewer outlier points.