# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 1 Introduction  
Three-dimensional reconstruction and visual representation is a broadly studied problem and can be used in many applications such as object recognition and scene understanding. State-of-the-art 3D reconstruction algorithms show important results and propose solutions to the Structure from Motion (SfM) and Simultaneous Localization And Mapping (SLAM) problems with important results and proposals that give a solution to these problems [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Techniques perform localization and mapping, and 3D reconstruction using active sensors (e.g., LiDAR scanners) and passive sensing (e.g., stereo cameras). However, in 3D reconstruction, none of these methods perform well on practical scenarios, and given the ambiguous correspondences between pixels and 3D spatial points, projection from 2D to 3D remains remarkably difficult and intuitive, these models are typically incapable of producing reliable matches in regions with repetitive patterns, homogeneous appearance, or large illumination change, a typical problem in photogramatry [12, 13, 14, 15]. The problem more challenging when working with aerial images of external environments. Nevertheless, with the current advancements in Deep Learning, it is possible to apply different architectures to obtain similar o better results using and combining different configurations of deep neuronal networks (e.g., Autoencoders). For example, using large existing datasets and considering sensor fusion of data, obtained from a stereo camera and an active 3D LiDAR [16; 17; 18], we are able to perform long-range depth estimation and 3D reconstruction. In this work, our main interest is that from input 2D aerial images, perform three-dimensional reconstructions. Due to the orographic conditions of the state of Oaxaca in Mexico, it is very difficult to obtain data from different areas. Therefore, a three-dimensional model would allow us to have information about the areas of interest. In particular, the conditions of the terrain of the Technological University of the Mixteca, in the highlands of Oaxaca state, Mexico, present variations in height and several areas large amount of homogeneous vegetation. Therefore, a three-dimensional model with point cloud generated with a neural network architecture from only 2D images as input would allow us to obtain important information about the university areas. Precise 3D reconstruction of the campus is important for several projects, ranging from infrastructure expansion to water recuperation in the roofs and green energy plants location passing for virtual tours proposed to new students. With a surface of 1.0 \(\times\) 10\({}^{6}\) m\({}^{2}\) and many high buildings in the campus, we need the support of aerial images to cover the dimension of the university and with the information provided from different perspectives, carry out a digital reconstruction. The motivation that drives us in this direction is an observation in the sense that the proposals in the current state-of-the-art do not focus in the use 2D aerial images. Furthermore, these proposals use information from other sensors like LiDARs [16; 17; 18]. On the other hand, current advances indicate that it is possible to combine different Deep Learning architectures to perform digital 3D reconstructions such as [19; 20]. Therefore, in this work, we propose to use an Autoencoder [21; 22; 23; 24; 25] architecture and Generative Adversarial Networks [26; 27; 28; 29; 30] with a 2D aerial images sequence as input to the proposed neural network architecture to obtaining as output a three-dimensional reconstruction with point clouds. Our contribution is summarized in two aspects:  
1. Based on an Autoencoder architecture, we propose a deep residual neural network for the Encoder stage and a GAN network for the Decoder stage. This configuration generates a point cloud using a sequence of 2D aerial images as inputs.