# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Digital Terrain Model Generation
#### 5.3.2 Quantitative Evaluation  
First, the Pleiades tri-stereo based DSM is compared to the LiDAR DSM (cf. Section 4.2.6). Due to the large temporal gap between LiDAR and Pleiades data acquisitions only selected areas are analyzed, which are not affected by temporal changes due to construction, vegetation growth or cloud cover. Mean values as well as STDs of the height differences are summarized in Table 14 for an area of 25.4 km\({}^{2}\). Here, the Pleiades based DSM has a bias of 0.64 m and thus, is actually too high. However, this is within the height uncertainty given for this sensor [19; 26; 132]. Consequently, the differences between LiDAR and Pleiades based DTMs will have the same bias, which however is not a result of the presented DTM generation algorithm. Second, the difference of the reference LiDAR DTM to the Pleiades DTM, which has been extracted using the proposed algorithm, is analyzed. The results are given in Table 14. The mean bias between both DTMs indeed is very similar to the one achieved for the DSMs. Because not all non-ground points are perfectly removed during the Pleiades DTM generation, the achieved DTM is locally above the LiDAR DTM, resulting in an additional mean height difference of 0.11 m. However, this bias is really \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Filter extent & 91 m \\ Height threshold & 3 m \\ Slope threshold & 30 degrees \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Parameters for terrain model extraction as used in all tests. Figure 25: Ortho CIR image (**a**) and relief shaded DSM (**b**) are visualized for a subset of the Tian Shui test site covering 3.4 km\({}^{2}\). small and it is below the absolute accuracy of LiDAR height measurement as well as Pleiades stereo height measurement. Figure 27: 3D view of (**a**) the DSM as shown in Figure 26 and (**b**) the resulting DTM after applying the proposed filtering method. Figure 26: Subset of (**a**) true-ortho image, (**c**) input DSM and (**b,d,e**) together with ground masks using different methods. Figure 29 shows DSMs and DTMs as generated from LiDAR and Pleiades data for two small areas of \(100\times 100\) m\({}^{2}\) each, as well as profiles for selected objects, representing (a) a skyscraper and (b) a round building, respectively. Analysis of the plotted profiles indicates that small structures like the buildup on the first skyscraper or the hole in the center of the round building, are not reconstructed using Pleiades data. The DTM generation is able to remove buildings and in some places our DTM looks even better than the LiDAR DTM, for instance, on the left border of the round building where the LiDAR DTM is even below ground level. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Mean [m]** & **STD [m]** & **Area [km\({}^{2}\)]** \\ \hline \(\Delta\)DSM & \(-0.64\) & 2.42 & 25.4 \\ \(\Delta\)DTM & \(-0.75\) & 1.10 & 25.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Statistics of height differences between LiDAR and Pleiades based DSM and DTM for the Innsbruck stereo data set. Figure 28: Urban subset (**left**) and suburban dataset (**right**). Shown are (**a,b**) ortho image, (**c,d**) extracted DSM, (**e,f**) derived DTM and (**g,h**) according nDSM in relief view. Overall, the resulting DTMs are visually appealing, which is also confirmed in the quantitative evaluation w.r.t. LiDAR reference data. Nonetheless, as long as the extraction of a DTM solely relies on the corresponding DSM, dedicated problem features and areas are inherent to the filtering algorithm being applied:  
1. First, large non-ground objects may not be filtered. In particular, if an object is larger than the DTM filtering size in all eight directions, it will remain in the DTM. 2. Second, this obviously holds for larger forests, where no ground point is visible. Such forest stands will also remain in the DTM. 3. Third, the presented robust slope estimation will fail on mountain peaks such that the minimum value will be determined incorrectly.