## 2 Proposed Methodology  
### Test Sites and Datasets  
We validated the performance of the proposed overlapping image-set determination method using two datasets captured under different conditions. Two target images were selected from each dataset to evaluate whether the proposed method effectively selected the overlapping images. For each target image, the actual overlapping images and their corresponding overlap ratios were measured to assess the efficacy of the method for constructing image sets from highly overlapping images. The proposed overlapping image-set determination method was evaluated and compared with conventional NoM- and BoVW-based approaches using two UAV image datasets captured under varying conditions. These two datasets were obtained using different cam era models and flight altitudes, resulting in different resolutions (Table 1). Dataset 1 is comprised of 386 images captured at an altitude of approximately 126 m over an urban area, with each image having a resolution of 5472 \(\times\) 3080 pixels. Dataset 2 consists of 394 images captured at an altitude of approximately 178 m over a construction site, with each image having a resolution of 8192 \(\times\) 5460 pixels. An overview of both datasets is presented in Figure 1. Dataset 1 was acquired through a linear flight predominantly along arterial roads in an urban area, resulting in insufficient overlap between images in certain sections along the flight path. The target areas included parking lots, vegetation, roads, and buildings of varying heights. In contrast, Dataset 2 was collected in a rural area with a grid flight pattern to survey construction sites, covering several roads, construction sites, and bare land. Dataset 2 exhibits repetitive characteristics due to the nature of the surveyed areas. In each dataset, two images with distinct shooting paths and visual characteristics were selected as targets to assess the image-set results. As shown in Figure 1, each target was chosen from flight paths with varying geometric structures. Figures 2 and 3 display individual target images. Multiple images were overlapped with varying ratios for each target, as shown in Figure 4. Constructing image sets comprising highly overlapping target images can enhance localization accuracy. Therefore, the performance of the proposed method was evaluated by comparing the actual overlapping images and their overlap ratios for each target and assessing its ability to select overlapping images compared with conventional methods. \begin{table}
\begin{tabular}{c c c} \hline
**Item Name** & **Dataset 1** & **Dataset 2** \\ \hline Flight height (m) & 126 & 178 \\ Survey area & Urban, arterial roads & Rural, construction sites \\ Camera mode & Yuneec E90X, 23 mm & DJI ZenmuseP1 \\ Focal length (mm) & 8.3 & 35.0 \\ Number of images & 386 & 394 \\ Image resolution (pixel) & 5472 \(\times\) 3080 & 8192 \(\times\) 5460 \\ Distinctive features & Limited image overlap in certain sections & Repetitive terrain features \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the UAV datasets. Figure 1: Localization of targets on structure from motion (SfM)-derived orthomosaics from UAV Datasets: (**a**) Dataset 1 with Targets 1 and 2; (**b**) Dataset 2 with Targets 3 and 4. As the targets were captured from different flight paths, the number and proportion of overlapping images varied for each target. Table 2 presents the dataset to which each target belongs, along with the maximum and minimum overlap area ratios. The first target was captured at the point where two straight paths intersected in Dataset 1, which included roads and parking lots. There were 34 overlapping images, with overlap ratios ranging from 90% to 8%. The second target was captured along a single path in Dataset 1, depicting roads and vegetation with 18 overlapping images and overlap ratios ranging from 90% to 8%. Target 3 was captured at a point where multiple straight paths intersected in Dataset 2, which featured a construction site.