## 2 Literature Review  
Over the years, numerous researchers have devoted significant effort to developing automated approaches for improving the reliability of deep learning in analyzing building characteristics from SVIs.  
It is widely acknowledged that including multiple augmentation techniques in a training dataset often leads to enhanced model performance--an idea considered a fundamental concept in the field. For example, [PERSON] et al. [1] developed an automated classification system for rural building characteristics using data augmentation to address the non-uniform sample collection but without detailing the specific methods employed. Their model achieved prediction accuracies of 95.9% for identifying abandoned buildings and 99.5% for determining the number of stories. Similarly, [PERSON] et al. [8] presented a deep learning approach for detecting gentrification indicators in SVI sequences, using rotation, shifts in height and width, zoom, shearing, and horizontal flipping to reach a test accuracy of 95.6%.  
[PERSON] et al. [9] proposed a method for detecting building characteristics, applying horizontal flipping and brightness/contrast/saturation/hue adjustments to their training set--yet did not assess these techniques' individual effectiveness--achieving final accuracies of 77.96% for foundation type, 83.12% for building type, and 94.60% for the number of stories. Lastly, [PERSON] et al. [10] introduced a landmark recognition approach that incorporated geo-information, employing random resized cropping, color jitter, and random flipping to attain a final accuracy of 97.5%.  
Across these studies, multiple augmentation methods contributed to the high accuracy levels, though there is still potential for further improvements. It is notable that most researchers applied augmentation without systematically demonstrating the individual impact of each technique. Empirical findings from various fields also indicate that combining multiple augmentation strategies does not always surpass the effectiveness of a carefully chosen single method [11; 12].  
A few investigations have explicitly examined how image augmentation influences certain tasks using SVI data. [PERSON] [13] tested nine techniques--brightness, hue, saturation, contrast, scale, translation, cutout, and weather augmentation, plus various combinations--for two tasks (window detection and window-state detection) in building-facade images, using different object detection methods and five backbone architectures (ResNet-50, ResNet-101, MobileNetV3, EfficientNetV2, and InceptionV3). The results showed that mixing multiple approaches did not invariably outperform a well-chosen single technique. In another example, [PERSON] et al. [14] applied rotation, horizontal flipping, Gaussian blur, and brightness adjustments for detecting building entrances in street view images, increasing the model accuracy from 61.2% (no augmentation) to 85.92% (rotation alone), 86.27% (three techniques), and 87.42% (all techniques). Collectively, these outcomes confirm that single or combined augmentation strategies can indeed outperform unaugmented data, yet merging multiple methods is not always superior [11; 12]. This underscores the importance of investigating augmentation approaches across varied and under-examined scenarios, including non-PPE detection at construction sites--a theme that remains largely unexplored.  
Although previous research has demonstrated that single or combined augmentation techniques can outperform unaugmented datasets, many opportunities remain in under-explored domains--particularly for automating building dimension estimation. To date, no study has specifically investigated how targeted image augmentation affects building detection and segmentation, where precise facade localization and boundary delineation differ from higher-level tasks such as classifying building stories or types. As a result, further evaluation of augmentation techniques in this context remains both relevant and valuable.