## 6 Conclusions  
This study examined how various image augmentation techniques influence building dimension estimation via object detection and instance segmentation. Specifically, eight distinct augmentations--brightness, contrast, perspective, rotation, scale, shearing, and translation augmentation, and a combined \"sum of techniques\"--were applied. For object detection, a Faster R-CNN was paired with three commonly used backbones (ResNet-101, ResNet-152, and MobileNetV3), while for instance segmentation, YOLOv10 was integrated with ViT, Swin Transformer, and PVT backbones.  
The results showed that contrast augmentation consistently yielded the highest gains in both bounding-box detection (Faster R-CNN) and instance segmentation (YOLOv10), based on both the AP50 and IOU metrics. Using all the augmentations simultaneously seldom outperformed the best single augmentation and in some cases, even degraded the performance. Shearing augmentation emerging as a second-best approach. Additionally, the validation and test results remained closely aligned, indicating minimal overfitting and suggesting that contrast-based improvements generalize effectively.  
While these findings highlight effective augmentation strategies for building dimension estimation, they may not necessarily generalize to other datasets or tasks with different data distributions. Nonetheless, the same techniques could be adapted for related objectives--such as classifying building ages or detecting windows--and the augmented models themselves may serve as practical, pretrained starting points for future work, particularly for accelerating the initial annotation of building features. Furthermore, because the augmentation effectiveness varied among different detectors and architectures, these results could guide the development of a weighted combination strategy, customizing augmentation selections to each model for optimal performance.  
One key limitation is the absence of advanced elastic augmentation techniques--such as GAN-based methods--which could produce richer and more realistic variations in texture and color. Incorporating these techniques in future work may result in more diverse training data that better capture real-world conditions. Additionally, because the dataset focused on a single metropolitan area in the UK, direct generalization to regions with markedly different architectural norms or climates remains uncertain. While this dataset serves as a valuable starting point for building-dimension research, gathering data from other locales--featuring varied construction types, environmental factors, and street-view scenarios--will be crucial to extend these findings to a broader context.  
Conceptualization, S.M. and S.W.; Methodology, J.-J.K. and S.M.; Software, S.M. and S.W.; Formal analysis, S.W.; Writing--original draft, D.H. and S.W.; Writing--review & editing, S.M. and S.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.