# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 4 Experimental Results
\[d_{Chamfer}(\hat{P},P)=\sum_{x\in\hat{P}}min_{y\in P}\|x-y\|_{2}^{2}+\sum_{y\in \hat{P}}min_{x\in P}\|x-y\|_{2}^{2} \tag{2}\]  
\[d_{EMD}(\hat{P},P)=min_{\phi:\hat{P}\to P}\sum_{x\in\hat{P}}\left\|x-\phi(x) \right\|_{2} \tag{3}\]  
The results of the three-dimensional reconstruction obtained with Pix4 DMapper and the results obtained with our proposal are shown in Table 2. To determine the similarity distance \(d_{Chamfer}\) and \(d_{EMD}\), between the results with the commercial software and our proposal, are used 1024 points samples, with 1400, 700, and 300 aerial images an overlapping percentage of 80%, 50%, and 30%, respectively, and 150 m height. The results show that the similarity is very close in percentages of 80% (the minimum percentage that commercial software requires to guarantee results). However, for lower overlapping percentages, commercial software cannot perform a three-dimensional reconstruction, while with the proposed methodology it may do. This causes the similarity metrics to begin to increase (see Figure 5). Moreover, the processing time of the proposed methodology (\(t_{proposal}\)) is considerably reduced compared to commercial software (\(t_{Pix4 DMapper}\)). With this proposed, the point cloud generated from aerial images will provide high-quality reconstruction in textures, meshes, and volumetric structures present in the several objects in the urban landscapes. Furthermore, similar enough to those obtained by Pix4 DMapper but with less processing time. In the second configuration, we use the same target areas used with the previous configuration. In this configuration we used the Grid Mission path and the results obtained are shown in Figure 5. The first column shows the results obtained with the Pix4 DMapper and the second column shows the results obtained using our proposed method. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Selected Area** & \(\mathbf{d_{Chamfer}}\) & \(\mathbf{d_{EMD}}\) & \(\mathbf{t_{proposal}}\) & \(\mathbf{t_{Pix4 DMapper}}\) \\ \hline Overlapping = 80\% & & & & \\ \hline Main courtyard & 11.99 & 18.51 & 50 min & 90 min \\ Laboratory buildings & 18.56 & 20.15 & 98 min & 180 min \\ Institute buildings & 21.56 & 32.15 & 120 min & 180 min \\ Classroom buildings & 18.79 & 20.23 & 80 min & 120 min \\ \hline Overlapping = 50\% & & & & \\ \hline Main courtyard & 41.99 & 48.51 & 30 min & 50 min \\ Laboratory buildings & 58.56 & 50.15 & 60 min & 80 min \\ Institute buildings & 41.56 & 62.15 & 80 min & 70 min \\ Classroom buildings & 58.79 & 40.23 & 50 min & 70 min \\ \hline Overlapping = 30\% & & & & \\ \hline Main courtyard & 51.99 & 45.51 & 30 min & 60 min \\ Laboratory buildings & – & – & 30 min & 60 min \\ Institute buildings & 61.56 & 62.15 & 40 min & 60 min \\ Classroom buildings & – & – & 30 min & 60 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the reconstruction results obtained using Pix4 DMapper and the results obtained using our proposal. The Metrics are computed on 1024 points. Additionally, the results are computed using 1400, 700, and 300 aerial images with 80%, 50%, and 30% overlapping percentage, respectively. The distances with no value indicate an overflow of the data. In contrast to the previous configuration, the results obtained from this experiment show a clear improvement in 3D reconstruction, when using our proposed methodology. Pix4 DMapper and other commercial software require special configurations, such as the flight path and camera configurations. If the configurations are slightly changed, as shown in our tests, the results become unfavorable. In comparison, the methodology presented in this paper is robust to these types of factors. This proposal does not depend on any especial configurations to be able to generate clear and legible point clouds. It is worth noting that our proposal is able to reconstruct areas that were not in the training dataset (areas outside the university campus) and Figure 6 shows an example.