# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 4 Experimental Results  
The proposal was evaluated using quantitative and qualitative measures, and these show how effective the model is for 3D reconstruction of urban landscapes. As the Pix4 DMapper is used in professional applications, its results were used as ground truth and they were compared with the results obtained using our proposed model. Taking into account the fact that most commercial software, such as Pix4 DMapper, DroneDeploy, and DJIGO4, have similar requirements to generate valid reconstructions and, therefore, to compare our results, we decided to use two experiments with different overlapping and height configurations. The first configuration uses a Circular Mission path with 80% overlapping and the second one uses a Grid Mission path with 50% overlapping. For both experiments, the images were taken at 4\(K\) resolution and at a height of 150 m.  
The results for the first experiment are shown in Figure 4, where the first column shows the results obtained with Pix4 DMapper and the second one shows the ones obtained with the proposed methodology. Each row contains the reconstructions of different target areas. The point clouds obtained show similar results, but the Pix4 DMapper software generates more accurate point clouds using this configuration. Analyzing the results, it is possible to observe that the proposed model generates a point cloud that represents both the compound shapes and textures and objects as trees, hallways, vehicles, and buildings. The generated point cloud is uniformly distributed and incorporates a considerable part of the selected area and the objects present. Figure 3: Accuracy and Loss graphs during training and validation for every stage of the proposed architecture, the Encoder stage (**a**,**b**), GAN-Decoder (**c**,**d**), and full proposed model (**e**,**f**). The results have shown that it is possible to recognize the features of each object in the scene. However, there are clear spaces separating the areas, which could be relatively important for the application, however, the results are visually acceptable, and compared to those obtained by commercial software they are valid enough. Furthermore, a quantitative analysis of the point cloud generated is performed using the Chamfer [55,56] distance (2) and Earth Mover's Distance EMD [57,58] Equation (3) as similarity metrics. The analysis of this evaluation allows recognizing the similarity of the reconstructions (the generated one and that of the commercial software) through the distance between points. In Equations (2) and (3), \(\hat{P}\) represents the point cloud generated with our proposal and \(P\) represents the one generated using the commercial software. To obtain the Chamfer distance, we find the sum of the squared distances obtained from each point and its closest neighbor. The chamfer distance is smooth and continuous in parts, and the search process is independent for each point. The lower value, the better and more accurate the similarity will be between the two point clouds. In the case of EDM, the bijection \(\phi:\hat{P}\to P\) is employed. In EMD, each point from \(\hat{P}\) corresponds to one unique  
Figure 4: (**–a**–h) 3D reconstructions. Comparison of the qualitative results obtained with Pix4 DMapper and the proposed architecture on the different areas of the UTM campus. From left to right: Pix4 DMapper or ground-truth and the point cloud generated from the proposed model. point in \(P\). In this way, it enforces a point-to-point assignment between the two point clouds. Table 2 shows the results of the distance between the point cloud generated by the proposed methodology and the one generated by the commercial software and the low \(d_{Chamfer}\) distances indicate their close similarity.