# Massive Data Capture Approach for Modeling Existing Building Stocks
## 3 Case Study
### Materials and Tools  
The application to the case study of the new methodology presented (in Section 2) had the following characteristics. In Step 1, only TLS, TIS, HQPs, and AFE were used as technologies for massive data capture because this provided a complete survey and reduced time by avoiding an unnecessary topo survey because, in this case, cartography and LiDAR data were available. The picture files were obtained using UAV DJI Mavic 3, as described in Table 2.  
For the site model, this project obtained the surrounding building heights from the vector files taken from the cartography, and the terrain from the LiDAR data because this was the most cost-effective option. In this case, the site information was the available CAD cartography from the Cartographic and Geological Institute of Catalonia (Institut Cartografic i Geologic de Catalunya [ICGC]) [74] and classified LiDAR point clouds from the same source to acquire the georeferenced terrain model information. In Step 2, this project applied the algorithms that were developed to.PTX alignment and frame extraction. In this case, the most efficient way to provide a reference was to use Satelite LiDAR data. Sub-step 2.1 was applied in two iterations. The first phase aligned all the TLS and TIS point clouds and applied the transformation matrix in the base files. Once the building point cloud had been obtained, it was aligned with the LiDAR data. The georeferenced matrix was obtained and applied to the building point cloud as well. Using this process, a registered point cloud was obtained that presents the advantages of being in a fixed position, aligned with the reconstructed 3D surrounding model, and being in an optimal input format for 3D reconstruction software tools. In this case, 983 HQPs were processed in a conventional way from RAW files into JPEG. For AFE, the project used 1197 frames extracted from 41\({}^{\prime}\) 28\({}^{\prime\prime}\) videos using the algorithm, as explained in Sub-step 2.3. Once the final images and the registered point cloud had been obtained, the files were imported into the software Reality Capture 1.3 to reconstruct the model. The Reality Capture software computes the photogrammetry model using the position of the laser scan, without modifying the registered position or the reference universal transverse mercator projection, in this case, ETRS89 UTM31N, EPSG:25831. The principal advantage is the usage of the same projection system as the official cartography. It also allows the methodology to be used worldwide. Following the third step, the first sub-step Poisson Reconstruction algorithm defines the detail in centimeters. This project runs the cleaning algorithm after exporting the Reconstructed 3D model from Reality Capture 1.3 software in the XYZ point cloud file. Then, this point cloud is meshed and filtered by distance before extracting the final point cloud in XYZ format. Finally, the topography and surrounding buildings are introduced as meshes in the BIM, which in this case, uses the Revit 2023 software tool [75]. To achieve this, this study used Blender BIM and Blender GIS [64]. The input data to the Blenders are the aforementioned cartography, the 1/1000 shape file cartography from the ICGC [65], and LiDAR [76]. The building information is introduced as point clouds after the previously