# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 5 Discussion and Conclusions  
In this work, a novel deep neural network architecture was presented for the generation of point clouds from aerial images of urban and natural landscapes. A notable contribution are the Autoencoder settings. The classical configuration was adapted with a  
Figure 6: Three-dimensional reconstructions of areas not in the UTM campus. The results obtained using our methody (**b,d**) are compared with commercial software (**a,c**). Figure 7: Three-dimensional reconstructions using aerial images taken at a height of 150 m (**a**), 200 m (**b**), 300 m (**c**), and with 400 m (**d**). These results show that our methodology performs 3D reconstructions at different high altitudes. However, at altitudes greater than or equal to 300 m, it presents some difficulties to perform three-dimensional reconstructions. residual network in the Encoder stage and a GAN network for the Decoder stage, which has been called GAN-Decoder. Using this architecture, it was possible to obtain results similar to those obtained using commercial software and, in some aspects, even superior. The proposed methodology is robust to variations of flight configurations for image acquisition. Fundamentally, this methodology does not depend on and does not need a special flight over the interest zone for the acquisition of information. Moreover, it is possible to obtain enough valid results with an inferior overlapping percentage in the images acquired and in less processing time. The results of this proposal are compared with the results obtained with commercial software. Having a Pix4D Mapper license allowed us to validate and compare our results. Our proposed methodology is robust to variations in flight configurations for images acquisition. Fundamentally, this methodology does not depend on and does not need a special flight over the interest zone during the acquisition of information. Moreover, we are able to obtain results in less processing time and using images with less overlapping percentage. Additionally, most works presented in the literature focus on three-dimensional reconstruction of controlled environments or solid and individual objects and uses fuse stereo images and point cloud from LiDAR sensors. In comparison, our proposal has focused on the three-dimensional reconstruction of urban landscapes just using from an aerial images sequence. In addition, we take advantage of the potential of GANs to distinguish true and false data without training data with many annotations. Therefore, we improved a discriminating network will make the generator network improve its data generation process. We have thoroughly trained a discriminate, based on the Adam's optimizer [50] and a maximization function (1), until the GAN can no longer distinguish true and false data. With which we have been able to solve the problem with the GAN models is its ability to trust the exit data. However, working in extreme conditions such as heights above 300 m and exploring areas with homogeneous textures remains a challenge. Conceptualization, A.L.R.-S., J.A.A.-A., H.T. and A.E.P.-B. ; methodology, A.L.R.-S., J.A.A.-A. and A.E.P.-B. ; software, A.L.R.-S.; validation, A.L.R.-S., J.A.A.-A., H.T. and A.E.P.-B. ; formal analysis, A.L.R.-S. and A.E.P.-B. ; investigation, A.L.R.-S.; resources, A.L.R.-S., J.A.A.-A., H.T. and A.E.P.-B. ; data curation, A.L.R.-S. and A.E.P.-B. ; writing--original draft preparation, A.L.R.-S., J.A.A.-A., H.T. and A.E.P.-B. ; writing--review and editing, A.L.R.-S. and A.E.P.-B. ; visualization, A.L.R.-S., J.A.A.-A. and A.E.P.-B. ; supervision, J.A.A.-A. and A.E.P.-B. ; project administration, J.A.A.-A. ; funding acquisition, J.A.A.-A. and A.E.P.-B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that support the findings of this study are available from the corresponding author, upon reasonable request.