This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

The implementation of a fast and efficient computer tool for field coverage studies in urban mobile radio systems is presented in this work. An accelerated and tailored ray launching method takes advantage of a ray tracing programmable framework optimized for massively parallel processing on GPUs. The PlotOptiX API is used to customize the code before applying the electromagnetic equations. The proposed code is described, and results are shown to demonstrate its correct operation. A high number of diffractions and reflections can be tracked in each ray from the transmitter to the receiver. In addition to the typical point-to-point simulation, measurement planes can also be set as receivers to provide fast predictions in wide urban areas.

This paper presents the development and improvement of a high-performance validated radio propagation simulation platform. The design and implementation process of the proposed wireless network planning tool aims to achieve the best human-computer interaction. It takes as a base a previously developed version that allowed the visualization of geospatial data and the use of empirical and semi-empirical algorithms to analyze macrocellular environments. Now, the application also allows the use of the ray launching technique to obtain accurate predictions in urban environments, visualize multiple raster data and display the results dynamically while they are being calculated. The implementation of a 3D geometry reconstruction system has also been included to allow the automatic loading of geospatial information provided by open-source tools such as OpenStreetMap and LiDAR. The 3D reconstruction system allows the user to run simulations of any part of the world. The proposed platform can optimize the position of base stations providing the best coverage based on the constraints provided. The optimization module allows the design and planning of new cellular networks and also the extension of existing networks. The result is an innovative high-performance radio propagation simulation platform that can provide multiple functionalities through a friendly and interactive web interface.