# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 4 Analysis of Experimental Data
### Analysis and Validation of Orthoimage RMSE  
Based on the images acquired from the Phantom4 RTK camera, an orthophoto was produced with PIX4D Mapper. As shown in the first step of Figure 3, an orthophoto was built using the method of a point cloud model. First, the images were aligned by analyzing the position of the camera for each image, and after entering the measured GCPs for geometric correction, the Align Photos step was performed to extract tie points from the overlapping area in the image.  
Through the Build Dense Point Cloud step, depth information was calculated from individual images based on the point cloud model built in the Align Photos step, to create a denser point cloud model as can be seen in the second step of Figure 3.  
Next, through the Build Mesh step, a polygon mesh model was created through the mesh-building process based on the dense point cloud model. Finally, through the Build Texture step, a step involving creating texture based on the original image and mapping the texture to the polygon mesh model, a 2D orthophoto was produced, as shown in the last step in Figure 4.  
Based on the image data taken with the UAV from an altitude of 100 m, which was an altitude expected to be commonly used in the field, the product acquired using Pix4D Mapper was analyzed. Figure 4 shows the orthophoto produced from the acquired data.  
To evaluate the accuracy of the image data captured, the RMSE value, which was the standard metric for the evaluation of accuracy for each of the 16 scenarios developed based  
Figure 4: Orthophoto of the study site created using UAV data.  
Figure 3: Process of 3D modeling for the study site.  
on the four GCPs, was calculated using software. The results derived through this process are presented in Table 3.  
In Scenario 1, the X- and Y-coordinate values of the RMSE were the following: (RMSE X, RMSE Y) = (0.161024 m, 0.120645 m); for Scenario 2, (RMSE X, RMSE Y) = (0.076477 m, 0.092455 m); for Scenario 3, (RMSE X, RMSE Y) = (0.054049 m, 0.081462 m); for Scenario 4, (RMSE X, RMSE Y) = (0.018381 m, 0.031209 m); and for Scenario 5, (RMSE X, RMSE Y) = (0.008412 m, 0.011855 m).  
Figure 5 shows a plot of the analysis results of the RMSE values of the X and Y coordinates for each scenario for images taken at an altitude of 100 m. According to the Aerial Photogrammetry Work Regulations of Korea, which were discussed in a previous study, the margin of error for 2D GCP is specified to be within \(\pm\)0.05 m based on map scales from 1/500 to 1/600.  
To further validate the accuracy of the orthophotos produced in each scenario, an additional analysis was conducted using checkpoints independent from the GCPs. Five checkpoints were separately measured within the study area, in addition to the four GCPs. For each scenario, the coordinates of the checkpoints on the modeled orthophotos were compared with the actual measured coordinates of the checkpoints, and the RMSE values were calculated to evaluate their accuracy. The RMSE values obtained from the checkpoint analysis corroborated the findings derived from the GCP analysis, providing further confidence in the reliability of the results. This additional validation step strengthens the conclusions drawn from the experimental analysis regarding the optimal number and distribution of GCPs required to achieve the desired accuracy for UAV-based orthophoto production in urban areas. Based on this validation, optimizing the number and placement of GCPs can not only improve accuracy but also provide benefits such as reducing field work time, saving labor costs, and increasing overall cost savings.