# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Surface Model Generation
The comparison to this old PCI version is just performed to emphasize the influence of the stereo matching algorithm as in this version a 2D matcher was implemented. Newer versions also use SGM and thus show similar results than ours. The DSM height values are scaled within 620 m to 680 m. When comparing LiDAR and Pleiades DSMs, temporal changes again occur, like a new residential area in the upper-left image area. It is obvious that the proposed workflow preserves 3D breaklines better than the workflow based on 2D matching. For instance, large buildings are decently reconstructed while they are missing in the 2D matching based DSM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Model** & **Mean [m]** & **STD [m]** & **Area [km\({}^{2}\)]** \\ \hline  & 1–2 & \(-\)0.62 & 4.53 & 10.8 \\ Trento & 2–3 & \(-\)0.55 & 1.97 & 10.8 \\ Triplet & 1–3 & \(-\)0.53 & 2.25 & 10.8 \\  & Triplet & \(-\)0.56 & 1.99 & 10.8 \\ \hline Innsbruck & & & & \\ Stereo & 1–2 & 0.25 & 1.68 & 26.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistics of height differences between LiDAR and Pleiades DSMs on non-forest regions. Figure 19: Detail view of a hospital at the Trento test site. For a partly forested region at test site Innsbruck with \(550\times 550\) m\({}^{2}\) Figure 21 shows a Pleiades ortho-image as well as the height differences between the LiDAR and the Pleiades DSM, scaled from \(-25\) m to \(+25\) m. Thus, bright areas indicate forest clear cuts, while dark areas correspond to forests growth between the LiDAR and the Pleiades acquisition dates. Analog to the Trento test site, a quantitative accuracy assessment was made for a selected area, which could be supposed to be free of temporal changes. The results are included in Table 11 and show slightly better accuracy than the Trento data sets. Overall, one very important outcome is the fact that the final DSM accuracy is worse w.r.t. the maximal possible values gathered through the sensor model optimization (cf. Table 6). Obviously, this decrease comes from the image matching step that on the one hand cannot yield the same accuracy as a manual point measurement. On the  
Figure 21: Detail view of a partly forested area at the Innsbruck stereo test site. Figure 20: Detail view of an urban area at the Innsbruck stereo test site. other hand in homogenous and repetitive image regions errors will occur or incorrect matches will just be interpolated. The last test set Ljubljana is the most interesting one, due to its multiple view geometry. The three along track stereo pairs can be used to form 15 along and across track stereo pairs as listed in Table 2. For all those 15 pairs DSMs are extracted and in addition for some selected combination of pairs. To be able to compare the coverages of different DSMs the threshold in fusion for populating an output DSM pixel is set to 1/3 of the input pixels. For example, if we have four DSMs and the fusion uses a \(3\times 3\) pixel kernel, then at least 12 pixels out of the 36 have to be valid such that an output is generated. This rather high threshold is chosen to allow a fair comparison of resulting nodata pixels. Note, that all remaining gaps could be filled by means of interpolation within matching or fusion. However, filling is purposely omitted to be able to recognize if multi-view images have an impact on the reconstruction completeness. Table 12 lists a multitude of statistical values, in particular minimal, maximal, mean, STD, MED, nmAD, mean absolute error (MAE), percentage of nodata values (i.e., 100% \(-\) completeness) and the convergence angles. Figure 22 complements the statistics and depicts the distributions of differences from LiDAR and Pleiades DSMs (we took the whole area of the LiDAR acquisition with 345 km\({}^{2}\) for the comparison as in References [130; 131]). It can be observed that there are huge outliers both positive and negative. They actually stem from the LiDAR reference data as there some height values in rivers are completely incorrect.