## 2 Materials and Methods
### Crack Localization  
Crack localization involves three methods: (a) Construction of spatial information using image processing techniques, (b) Merging of spatial information layers based on point cloud technique and image stitching technique, and (c) Crack localization and Data validation. In this study, spatial information was constructed for estimating the location of cracks in concrete building exterior walls using image stitching and Point Cloud techniques. The Point Cloud technique is one of the methods for constructing 3D data and represents objects or environments as a set of points. Each point represents a position in 3D space, and the combination of points forms an overall model [35]. The typical process of spatial information construction based on Point Cloud techniques consists of three steps: (1) Initial Processing, (2) Point Cloud and Mesh, and (3) Digital Surface Model (DSM) and Orthomosaic. In the initial processing, the Scale Invariant Feature Transform (SIFT) algorithm is used to identify key points as feature points in images that contain location information. Then, matching is performed to find corresponding key points in different images, and the internal and external parameters of the imaging sensor are calibrated. Next, the generated key points undergo Point Densification to construct the Point Cloud. Based on the constructed Point Cloud, a 3D Textured Mesh can be created, allowing the construction of a 3D model. Using this 3D model as a foundation, DSM and orthomosaic can be generated [36]. Aerial images acquired using unmanned aerial vehicles (UAVs) contain vectorized location information. The spatial information constructed based on the Point Cloud technique using these aerial images has high accuracy and realism because it includes location information [37]. However, the spatial information built based on the point cloud technique causes a situation in which the image quality is lower than that of raw data in the process of generating a 3D model by dividing and reconstructing aerial images into points [38]. As a result, while the spatial information constructed based on the Point Cloud technique retains location information, it has limitations in defining cracks due to the difficulty in detecting cracks at the millimeter level. The image stitching technique is a method that combines multiple images by matching common feature points to create a single image, allowing for the acquisition of high-resolution images like panoramic images. Feature-based image stitching involves extracting geometric features such as corners, edges, and lines from the input images and comparing them with the reference image to find corresponding points [39]. These characteristics have robustness against brightness variations. However, extracting too many feature points increases the computational workload, leading to slower processing speed. Additionally, if incorrect feature points are extracted, errors may occur during the matching process. Therefore, in feature-based image stitching methods, the extraction of feature points is the most crucial factor to consider [40]. The typical image stitching-based spatial information construction consists of five steps: (1) Feature Point Detection and Matching, (2) Correspondence Point Matching, (3) Estimation of Transformation Model, (4) Image Blending, and (5) Image Generation. Firstly, a feature point detection algorithm is used to detect common feature points among input images. Then, the detected feature points are used to match corresponding points between images. Correspondence points represent points connected between feature points in one image and feature points in another image. Next, using image transformation algorithms, relative position, rotation, and size information between images are utilized to estimate the transformation model based on the correspondence points. Using this process, multiple images are combined according to the transformation model to generate a single spatial information image formed using image stitching [41].