## 1 Introduction  
Accurate measurements of building dimensions underpin a broad range of applications. For instance, they supply critical inputs for building performance simulations (e.g., heating, cooling, and insulation needs) and help assess a structure's contribution to the urban heat island effect, given that larger buildings with extensive rooftops and facades can absorb and retain more heat [1]. A variety of automated methods can be used to estimate building dimensions (particularly width and height), including Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), and Street View Image (SVI) approaches [2]. The SVI approach is frequently favored due to its cost-effectiveness and ground-level perspective, making it suitable for numerous real-world applications. One prominent example is Google StreetView (GSV), which covers over 16 million kilometers across 83 countries; by simply entering geographic coordinates or addresses, users can retrieve these images [3]. In a typical SVI-based workflow, the building's latitude and longitude are combined with the camera location to determine the distance between the two (e.g., via the Haver-sine formula). A pinhole camera model then converts image-based measurements into real-world dimensions, mapping 2D building boundaries (either bounding boxes or segmentation outlines) to the actual physical size [4]. In many studies, deep learning methods are used to identify building boundaries in images through two major approaches: object detection and instance segmentation. Object detection localizes and classifies buildings using bounding boxes, requiring relatively light computation and simpler labeling. In contrast, instance segmentation assigns class labels at the pixel level, resulting in more accurate boundary outlines--particularly for complex facades--though at a higher computational cost due to the extra pixel-wise processing [5]. Each method is valuable in different scenarios: bounding boxes suffice for coarse dimension estimates, while pixel-level segmentation provides precise facade delineation where more accuracy is needed. However, deep learning techniques demand large and diverse datasets to robustly train the models. Each parameter is refined by minimizing a loss function that compares predictions to the ground truth, meaning that insufficient or homogeneous training data often lead to poor generalization [6]. To address the challenge of limited data diversity, image augmentation is a potent strategy to enrich the original training data set without the need to gather new data. This approach expands the original data's breadth by applying a suite of modifications to the existing images, thus producing a variety of novel and diverse samples [7]. Such modifications include an array of transformations like rotations, flips, scaling, and color adjustments, all designed to emulate the range of variations that could naturally occur in real-world environments. By resolving the issue of insufficient features within the original dataset through image augmentation, the model's capacity for generalization is significantly bolstered. However, if the augmented data are unrelated or do not accurately reflect realistic feature variations, the model might require more computational resources to process the increased number of training examples. Moreover, the presence of irrelevant features can dilute the influence of relevant features in the model. The model might not weigh the relevant features as heavily if it is also trying to incorporate irrelevant ones, which can result in poorer performance [5]. Thus, it is crucial to employ augmentation methods that are carefully selected to represent plausible real-world scenarios. This paper introduces image augmentation techniques derived from SVIs through the application of deep learning methods that aim to improve the accuracy of estimation of building dimension based on the object detection method and instance segmentation method. The main contributions of this study can be summarized as follows:  
1.