# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Surface Model Generation
We also plotted STD, nmAD and nodata percentages versus the convergence angles as depicted in Figure 23. On the left side, the plots are given using the complete error distribution including all gross outliers. On the right side, only errors with absolute values smaller than 6 m are used such that gross outliers are removed. At first, it can be seen that the STD trends change completely in these two experiments. While using all data the STD is directly correlated to the convergence angle, it is indirectly correlated when removing the gross outliers. In addition, the value range of the STD changes drastically. This behavior again shows that the STD estimate is indeed not robust by any means and should be avoided when interpreting DSM differences. The NMAD plots on the other side yield the same trend and comparable number ranges for both experiments. As expected the nmAD decreases (and thus the accuracy of the DSMs increases) with increasing convergence angles. The percentage of nodata values is directly correlated with the convergence angle and, obviously, the number of nodata values increases in the second experiments as outliers are removed. Next, combinations of stereo pairs are processed. Groups with small intersection angles in the range of 10\({}^{\circ}\) to 20\({}^{\circ}\) (i.e., pairs 1-3, 2-4, 3-5 and 4-6), medium angles in 20\({}^{\circ}\) to 30\({}^{\circ}\) (i.e., pairs 1-2, 1-4, 1-5, 2-3, 2-6, 3-4, 3-6 and 5-6) and large angles in 30\({}^{\circ}\) to 40\({}^{\circ}\) (i.e., pairs 1-6, 2-5 and 4-5). Here, the medium angles perform best, seen on the low nmAD and low nodata values. Only smaller or only larger angles degrade the resulting DSM. When using all 15 pairs, obviously, the nodata regions are lowest with only 1.1% (i.e, a completeness of 98.9%). Note, that the reference LiDAR data holds 0.44% of nodata values, such that the real completeness is even higher than reported. Also the nmAD of all pairs is decently small, however it is even lower for, for example, the pairs in medium angle range. Here, we conclude that stereo pairs should be selected within a certain intersection angle range, where 20\({}^{\circ}\) to 30\({}^{\circ}\) seem optimal (which correspond to a B/H ratio in the range from 0.35 to 0.55). Two additional combinations are processed, namely 1-2 & 3-4 and 1-2 & 3-4 & 5-6, that is, only along track stereo pairs. As all those pairs have convergences angles around 25\({}^{\circ}\) the results are quite good, where the nmAD of the triple along track pair is even the best. Figure 22: Distribution of differences from LiDAR and Péiades DSMs for all 15 stereo pairs of the Ljubljana test site. The error range of \(-6\) m to \(+6\) m is shown. Figure 24 visualizes the differences in various DSMs. Next, to the reference LiDAR DSM the Pleiades DSM is shown based on the fusion of all stereo pairs with convergence angles in from 20\({}^{\circ}\) to 30\({}^{\circ}\). For comparison also the DSM from the good stereo constellation 1-2 is depicted and from the worse performing constellation 1-3. At first, we see that the LiDAR model holds information like the power lines that cannot be reconstructed from Pleiades images (but nevertheless contributes to our error metric). From the Pleiades DSMs the fused one is visually a lot better, since it contains less gross errors (cf. the roof of the hall in the lower part of the image), it is more complete and it is also smoother. Comparing only the single stereo results the pair 1-2 yields a smoother surface than 1-3. Additionally, the roof structure of the central large building is not reconstructed from the 1-3 pair. Overall, this visual comparison supports our assumption that using multi-view Pleiades sets allow higher accurate 3D modeling than single stereo sets. Figure 23: Plots showing (**a**,**b**) STD, (**c**,**d**) nmAD and (**e**,**f**) percentage of nodata values plotted versus the convergence angle for the Ljubljana test site.