# UAV Application for Typhoon Damage Assessment in Construction Sites
## 4 Case Study and Process Design
### Three-Dimensional Modeling at the Construction Site  
Pix4 dmapper is an aerial survey software developed by a Swiss company. It is used to process images taken by UAVs. It has the advantages of high professional precision and automatic processing. It can automatically correct the camera and obtain high-precision 3D point cloud data and enables fast image processing. Therefore, pix4 dmapper software was used for UAV image processing to achieve the rapid 3D model establishment of construction sites. In the pix4d software, the process of processing the UAV image to obtain the point cloud model is shown in Figure 6.  
Firstly, the filtered image data were imported, including images taken by the UAV and POS data files. The POS data file contained the heading angle, pitch angle, roll angle, and the longitude, latitude, and elevation of the pictures taken during the flight operation of the UAV. The image path of the UAV was automatically generated after the image data were input. The input and output coordinate system was selected; this experiment used the CGCS2000 default coordinate system. Through the localization processing of image data, according to the collected 2D image data, the required 3D point cloud data of the construction site were synthesized by the principle of aerial triangulation photography of unmanned aerial vehicles.  
Then, for point cloud and texturing, the photo location was displayed in green, indicating that the location information was correct, and the processing operation was started. The processing of point cloud data could be divided into three parts: point cloud synthesis, preprocessing, and segmentation. The first step was to synthesize the 3D point cloud. The imported images were aligned to generate sparse point clouds and calibrate the sparse point clouds through optimization. In order to perform point cloud segmentation later,  
Figure 5: The image of the construction site collected by UAV.  
upsampling was used to encrypt the point cloud data to meet the design requirements of processing. The second step was point cloud preprocessing. The point cloud was down-sampled by the methods of through filtering and voxel grid filtering, and the interference information and noise points in the point cloud data were filtered. The third step was point cloud classification and segmentation. The construction site scene of the construction site was complex, and the amount of point cloud data was large. In this study, the random sampling consistency segmentation algorithm was used to classify and segment the point cloud data, and the distance threshold parameter was set to 1.  
Finally, the output model results and quality report were obtained. Figure 7 shows the final generated digital orthophoto map (DOM) and digital surface model (DSM). The DOM could visually see the situation of the entire construction site, and the DSM image could digitally express the elevation of the construction site, where the blue part represented the lower area and the red part represented the higher elevation area. The quality report played a feedback role in the process of point cloud processing and model establishment, made targeted solutions to those that were not qualified in the quality report, and finally obtained a qualified quality report. The 3D model was not much different from the live pictures, and the quality report showed that the image calibration was 100% with all images enabled. The deviation between the internal and optimized camera parameters was 2.61%, the average ground sampling distance was 1.64 cm, and the coverage area was 68,400 m\({}^{2}\).  
Figure 6: Pix4D mapper UAV image processing flow.  
Figure 7: The image of the digital orthophoto map (DOM) and digital surface model (DSM).