# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 2 Related Work  
Unmanned aerial photogrammetry with UAV-based systems is a relatively low-cost method compared to traditional manned aerial photogrammetry and is capable of fast and efficient data acquisition and processing for surveying, even for places for which human access is difficult or over large areas. The distinctive advantages of UAV platforms have attracted continuous research interest such as producing orthophotos with UAV imagery over a wide range of applications. An orthophoto refers to an image from which image displacement caused by terrain relief and camera orientation at the time of taking the image has been removed. Upon the process of correction, an image is expressed as a vertically looking-down image. Orthophotography can be utilized for the inspection of buildings/facilities, analysis of land cover/land use, etc., and the anticipated effects include cost saving from reducing the number of field surveys to the facilitation of visual and geospatial analyses in construction projects such as in the case of urban planning. According to previous research, orthophoto production in units of cities or districts without GCPs often generates many errors. The installation of GCPs helps to guarantee a certain degree of precision and minimize the occurrence of errors. Furthermore, the efficient placement of GCPs against the entire area of the target site, which plays a key role in orthophoto production, is expected to reduce the time involved and increase cost-effectiveness. In Korea, according to the current version of the Guideline for Public Survey Using Unmanned Aerial Vehicles, the requirements on the installation of the target signboards for aerial surveying and the number of GCPs are set as nine points per km\({}^{2}\) in principle. The placement or arrangement of GCPs is also described in the provision in the Guidelines, but in this study, we aim to achieve efficiency in obtaining the results with high accuracy using a small number of GCPs by modifying the number and distribution of GCPs specified in the Guidelines. Considering the previous studies to date [13; 14], although there have been comparative evaluations or analyses on the impact of having no GCPs or GCPs on the RMSE values of X, Y, and Z coordinates, few studies have investigated the impact of the number and distribution of GCPs on the accuracy of the resulting orthophoto. Therefore, to facilitate active utilization in practical applications, more in-depth research is needed on the impact of the number and distribution of GCPs. Thus, in this study, we aim to analyze the impact of the number and distribution of GCPs on the accuracy of UAV-based orthophoto production, ultimately explaining the impact on the quality of the survey results. In addition, based on the results obtained, we propose the optimal number per unit area and the optimal distribution of GCPs. Realizing an increased accuracy in UAV-based photogrammetry will enable UAV-based platforms to be more extensively utilized in diverse applications such as process control at construction sites or during facility inspection, surveying, building status surveys, and investigations of unauthorized buildings. First, for a brief overview of previous studies related to GCPs in UAV photogrammetry, [PERSON] et al. [15] reported the installation of nine GCPs at points per 1 km\({}^{2}\) in accordance with the regulations of Korea. [PERSON] et al. [16] installed three GCPs in the test site of approximately 0.2 km\({}^{2}\), and the orthophoto and DSM results were within the margin of error. [PERSON] et al. [17] proposed a method of automatically creating a building model using a UAV-based point cloud and reported that the method can be applied not only to update two-dimensional (2D) data such as digital maps, but also for the three-dimensional (3D) modeling of buildings based on 2D data. [PERSON] et al. [18] conducted experiments using eBee-RTK (SenseFly), which is a survey-grade fixed-wing drone, and installed 12 GCPs in the test site over an area of about 0.2 km\({}^{2}\).