### Previous Studies  
The methods for selecting overlapping UAV images can be broadly categorized into two types: prior knowledge-based methods and visual similarity-based methods [12]. Prior knowledge-based methods utilize data from the positioning and orientation system (POS), which comprises GNSS and IMU devices mounted on a UAV, to select overlapping images. By contrast, visual similarity-based methods estimate image similarity based on feature information to select overlapping images. A detailed explanation of each methodology is provided below. Prior knowledge-based methods select overlapping images based on the observation that images with close POS locations typically exhibit a higher spatial overlap. This approach, widely used to determine overlapping UAV image sets, offers simplicity and high computational efficiency [15]. For example, [PERSON] et al. (2021) utilized the POS and elevation data to select overlapping images based on their footprints [16]. However, it is challenging to adapt prior-knowledge-based methods when the GNSS positional accuracy is insufficient or when GNSS information is unavailable. Consequently, many researchers have shifted their focus towards visual-similarity-based overlapping image selection. Visual similarity-based methods estimate the similarity between UAV images based on the features extracted from the images and select the overlapping images. Therefore, they offer the advantage of constructing overlapping image sets without relying on prior knowledge, which makes them applicable to various data acquisition environments. These methods can be further subdivided into number of matches (NoM) approaches, which utilize the count of matched keypoints, and clustering-based image-retrieval methods [12]. NoM methods evaluate the similarity based on the number of matched keypoints, with a higher count indicating greater similarity. This approach is based on the principle that images with a high degree of overlap will share numerous common features, resulting in a larger number of matched keypoints between them. The methodology involves conducting feature matching across all images using algorithms such as scale-invariant feature transform (SIFT) or speeded-up robust features (SURF). Images with a substantial number of matches are identified as highly overlapping. NoM-based methodologies have been utilized in various studies because of their relatively straightforward application and expectation of highly accurate overlapping image results. [PERSON] and [PERSON] (2016, 2018) proposed a methodology for enhancing the efficiency of NoM-based methods by conducting stepwise feature matching through down-sampling and original image processing to select overlapping images [17; 18]. [PERSON] (2013) suggested sorting the features of each image by size, performing matching only for the top-scale features, and considering an image set with matches above a certain threshold as overlapping [19]. Furthermore, he applied the proposed methodology as an overlapping image-set determination method for the incremental Structure from Motion (SfM) software VisualSfM (version 0.5.26) [20]. Image retrieval is a process involving extracting features from images and clustering them into high-dimensional vectors, which are then utilized to identify overlapping images. The vocabulary tree is a method that converts features into bag of visual words (BoVW) vectors and is widely used in selecting image sets for localization in SfM software, such as ColMap, AliceVision, and Pix4 Dmapper [21; 22; 23]. BoVW is an approach that represents an image as a histogram of visual words, which are quantized representations of local image features. This method facilitates efficient image comparison and retrieval by transforming complex image data into a more manageable form. The efficiency and flexibility of the vocabulary tree have been leveraged in research to achieve efficient overlapping image set selection in oblique aerial large-scale datasets [9; 21; 22; 23; 24; 25]. [PERSON] et al.