# Mapping with Pleiades--End-to-End Workflow
## 5 Results
### Sensor Modeling and Parameter Optimization
Over-determination as exemplary given, for example, by utilizing 10 GCPs, reduces these systematic errors to a more or less negligible order of magnitude and yields distinctly improved 3D RMS accuracy values, widely adequate for both optimization scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Study** & **Model** & \(\delta\)**[\({}^{\circ}\)]** & **GCPs** & \multicolumn{3}{c}{**RMS Initial [m]**} & \multicolumn{3}{c}{**RMS Adjusted [m]**} \\
**Area** & & & & **E** & **N** & **H** & **L** & **E** & **N** & **H** & **L** \\ \hline Innsbruck & \multirow{2}{*}{1–2} & \multirow{2}{*}{26.9} & \multirow{2}{*}{30} & \multirow{2}{*}{4.85} & \multirow{2}{*}{2.07} & \multirow{2}{*}{3.45} & \multirow{2}{*}{6.28} & \multirow{2}{*}{0.38} & \multirow{2}{*}{0.23} & \multirow{2}{*}{0.59} & \multirow{2}{*}{0.73} \\ Stereo & & & & & & & & & & & \\ \hline Innsbruck & 1–2 & 10.1 & 20 & 5.26 & 4.78 & 1.46 & 7.26 & 0.33 & 0.46 & 1.18 & 1.31 \\ Triplet & 2–3 & 23.3 & 20 & 5.44 & 5.10 & 2.79 & 7.96 & 0.29 & 0.39 & 0.56 & 0.74 \\ East & Triplet & 33.4 & 20 & 5.33 & 5.14 & 2.31 & 7.76 & 0.28 & 0.39 & 0.47 & 0.67 \\ \hline Innsbruck & 1–2 & 24.9 & 16 & 3.48 & 1.68 & 6.09 & 7.21 & 0.36 & 0.19 & 0.76 & 0.86 \\ Triplet & 2–3 & 4.6 & 16 & 3.85 & 2.07 & 4.38 & 6.19 & 0.59 & 0.36 & 2.01 & 2.13 \\ Center & Triplet & 29.5 & 16 & 3.41 & 1.64 & 5.83 & 6.95 & 0.33 & 0.17 & 0.73 & 0.82 \\ \hline Innsbruck & 1–2 & 10.4 & 22 & 5.03 & 4.72 & 2.17 & 7.24 & 0.19 & 0.33 & 1.26 & 1.32 \\ Triplet & 2–3 & 23.0 & 22 & 5.17 & 4.88 & 3.45 & 7.90 & 0.26 & 0.27 & 0.98 & 1.04 \\ West & Triplet & 33.4 & 22 & 5.09 & –4.96 & 2.97 & 7.70 & 0.21 & 0.25 & 0.67 & 0.75 \\ \hline  & 1–2 & 5.5 & 21 & 4.75 & 8.14 & 6.40 & 11.39 & 0.94 & 0.88 & 3.35 & 3.59 \\ Trento & 2–3 & 27.3 & 21 & 4.52 & 7.66 & 3.88 & 9.70 & 0.36 & 0.34 & 1.15 & 1.26 \\ Triplet & 1–3 & 32.9 & 21 & 4.60 & 7.60 & 4.16 & 9.81 & 0.42 & 0.31 & 1.15 & 1.26 \\ Triplet & 32.9 & 21 & 4.70 & 7.66 & 4.07 & 9.87 & 0.38 & 0.31 & 1.11 & 1.21 \\ \hline Ljubljana & \multirow{2}{*}{1–2} & \multirow{2}{*}{25.2} & \multirow{2}{*}{28} & \multirow{2}{*}{0.99} & \multirow{2}{*}{0.71} & \multirow{2}{*}{2.72} & \multirow{2}{*}{2.98} & \multirow{2}{*}{0.29} & \multirow{2}{*}{0.30} & \multirow{2}{*}{0.28} & \multirow{2}{*}{0.50} \\ Stereo & & & & & & & & & & \\ \hline Ljubljana & \multirow{2}{*}{3–4} & \multirow{2}{*}{22.7} & \multi  
The second test is based on the Ljubljana set, where the 3D geo-location accuracy is evaluated using GCPs and ICPs with the results depicted in Table 8. The initial RMS values reveal that the 2D geo-location residuals directly propagate to the resulting 3D geo-location accuracies. Thus, all stereo pairs containing the images 5 or 6 yield large displacements (also cf. Table 5). After adjustment, the majority of pairs yield high accuracies for the GCPs in planimetry of 0.2 m to 0.3 m and also in height of 0.2 m to 0.4 m. Actually, an accuracy at this level was never achieved before and is based on the highly accurate reference data. The statistics of ICPs are, as expected, a bit worse but no overfitting is observed. Figure 18 shows the 3D errors for GCPs and ICPs sorted w.r.t. the ICPs. Interestingly, there are four stereo pairs holding lower accuracy in height (and thus also in the 3D length), namely the pairs 1-3, 2-4, 3-5 and 4-6. When compared to the acquisition disposition depicted in Figure 6 all those pairs are pure across track images from two adjacent orbits with small convergence angles of about 12.5\({}^{\circ}\), which is the reason for the poor height estimates.