# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 5 Discussion  
In this study, the impact of GCPs on the accuracy of UAV photogrammetry was examined, and the results indicated that at least three GCPs were needed for the optimal improvement in the image accuracy. This finding contrasts with the result reported by [PERSON] et al. [26], who proposed that eight to nine GCPs were required for a similar level of accuracy for a target area of 342 ha. The methodology presented in this study, which focuses on various types of topographic features, proposes a more efficient approach in terms of GCP placement without compromising quality. The results of this study indicate that the new GCP configuration that was tested can significantly improve the efficiency and accuracy in the practice of UAV photogrammetry. This study focused on cost-effective and rapid map production by achieving the desired level of geolocation/positioning accuracy  
Figure 5: RMSE by scenario.  
through the installation of a minimal number of GCPs. The GCP installation standards were set based on the Aerial Photogrammetry Work Regulations of Korea. Contrary to the findings obtained from a previous study by [PERSON] and [PERSON] (2020) [27], which emphasized the placement of GCPs over the number of GCPs, our findings indicate that the strategic placement of GCPs can reduce the need for the installation of more GCPs, resulting in the provision of a cost-effective solution for UAV photogrammetry. In addition, the findings of this study underline the importance of the balance between the number of GCPs and their strategic placement, presenting a new perspective on the optimization of photogrammetry projects. In this study, we analyzed the influence of the number and distribution of GCPs on the accuracy of UAV-based orthoimages. The experimental results showed that a minimum of three GCPs could achieve the desired level of accuracy. Future research could further optimize the GCP placement strategy by considering various factors such as terrain and feature characteristics, flight altitude, and overlap ratios. Employing machine learning techniques to automatically explore optimal GCP locations could also be explored.  
This finding is consistent with the results of [PERSON] et al. (2022) [28]. In their study, they experimentally analyzed the impact of GCP height differences and geometric distribution on the positional accuracy of UAV point cloud data. They found that larger height differences between GCPs and clustered GCP configurations led to lower positional accuracy of the point cloud data. These results suggest that an appropriate vertical and horizontal distribution of GCPs is crucial for improving accuracy. In our study, we were able to obtain accurate results by evenly distributing GCPs throughout the entire study area. The findings of [PERSON] et al. (2022) provide practical guidelines for establishing an optimal GCP distribution strategy.  
However, according to other previous research ([PERSON] et al., 2017 [24]), accuracy improvement diminishes when the number of GCPs increases beyond a certain level. In our study, the accuracy improvement was not significant when more than four GCPs were used. Therefore, it is important to develop a distribution strategy that achieves the target accuracy with the minimum number of GCPs, considering the size and topographic characteristics of the study area.  
In conclusion, by synthesizing the results of our study and several previous studies, it is evident that both the vertical/horizontal distribution and the total number of GCPs influence accuracy. The most efficient approach for producing UAV-based orthoimages would be to establish a distribution strategy that satisfies the accuracy requirements with the minimum number of GCPs, taking into account the characteristics of the study area.