## 3 Experimental Results
### Crack Localization  
In this study, a background model was generated in the target building using the Point Cloud technique based on aerial photography images with recorded location information. The spatial information construction using the Point Cloud technique was carried out using Switzerland PIX4D company's PIX4D mapper software (Prilly, Switzerland). The following Figure 8 shows the spatial information construction process for the background model generation.  
A total of 107 aerial images were used to build spatial information using the Point Cloud technique for creating a background model, and the location accuracy of the constructed spatial information was RMSE X = 0.65 m, RMSE Y = 0.33 m, and RMSE Z = 0.40 m.  
Next, the spatial information of the target building was constructed using the image stitching technique to generate high-resolution images and acquire crack expression information. The image stitching technique for constructing spatial information applied a feature-based stitching technique that utilizes the feature points of each image and used a data set previously constructed through image distortion correction and feature point extraction. The spatial information of the target building created by using the image stitching technique to acquire crack expression information is shown in Figure 9.  
Next, the analysis data was constructed by merging the spatial-information-based background model (using the Point Cloud technique) with the location information obtained using image stitching, which expressed the crack locations in high resolution. The merging of the spatial information layers was performed using AutoCAD 2023 software from Autodesk in San Francisco, CA, USA. The merging was achieved by matching the relative coordinates of the stitched image data, where the crack locations were expressed in the orthomosaic of the building, with coordinate values obtained using the background  
Figure 8: Process of spatial information construction using Point Cloud technique.  
Figure 7: Result of feature point detection and matching using SIFT algorithm.  
model. First, standardization was performed by adjusting the 1:1000 scale orthomosaic. After that, to overlap the orthomosaic and stitching image data of the target building, the starting point of the lower left corner of the building was set as the reference point (0, 0), and the relative distance from the reference point was converted into (x, y) coordinates to estimate relative location information. Superposition was performed using the analysis of data built using layer merging, which is shown in Figure 10.  
Using the analysis data established by merging layers, four cracks were identified in the building at the target site, and their locations were estimated. To estimate the crack locations, we set the lower left end of the target building as the reference point (0, 0) in the constructed analysis data. The location information of the concrete cracks was then estimated by converting the reference point and the crack locations into relative distance coordinates (x, y). Therefore, to estimate the relative location of the cracks, we determined the starting point (x1) and ending point (x2) of the horizontal axis crack and the starting point (y1) and ending point (y2) of the vertical axis crack from the reference point (0, 0) of the building. In this study, we expressed the vertical/horizontal start and end points of the crack as (x1, y1) and (x2, y2) coordinates to estimate their relative positions. The estimated relative locations of the concrete cracks based on the scale are shown in Table 1.  
Figure 9: Result of spatial information using image stitching technique.