# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Building Damage Classification
#### 3.1.1 Binary Classification
[31] proposed an automatic damage detection method for historic structures, employing a CNN-VGG16 model for image classification and feature extraction. The study focused on detecting five damage typologies in historic stone structures (i.e., material loss, erosion, color change, natural, and sabotage), which were used in the training phase to develop a binary model able to accurately predict the presence or absence of any damage form, with an average accuracy of 98.92%. On the other hand, other works have focused on different types of materials. For instance, [PERSON] and [PERSON] [32] showed interest in surface damage on wooden architectural cultural heritage, addressing the lack of research in this area. Using a CNN-based model, the system enabled real-time on-site monitoring and automatic damage typology detection based on images taken directly from cultural heritage sites in South Korea. Considering the most prevalent pathologies of wooden structures, the model was trained with images related to forms of exfoliation, deterioration, and cracks. The binary classification methodology adopted allowed the model to output predictions regarding the presence or absence of any defect it was trained on. Four models were used and compared--VGG16, InceptionNetV3, ResNet50, and EfficientNetB0--among which the latter performed with better accuracy indices. Unlike other studies, [PERSON] and [PERSON] [33] introduced a deep learning framework for detecting the slight roof tilts of a traditional wooden structure typical of South Korea. The proposed method involved binary normal-abnormal classification using images derived from video data acquired from CCTV cameras, with six environmental datasets for context condition generalization. Four Deep Learning models based on pre-training were employed: EfficientNetB0, EfficientNetB2, ShuffleNet_v2. and AlexNet. Among these, EfficientNetB0 and EfficientNetB2 models showed high prediction accuracy of 99.61% and 99.55%, respectively. Meanwhile, [PERSON] et al. [34] proposed a novel strategy for the detection, segmentation, and measurement of glazed tiles from the roof of the Palace Museum in China. The proposed architecture had two levels: the first employed Faster R-CNN with ResNet101 to automatically detect and crop the glazed tile photographs with high accuracy; these cropped images formed a dataset for training the Mask R-CNN algorithm with the ResNet101+ FPN (+RPN) model, which was used in the second level to segment and measure the damage detected on the tiles, with an average accuracy of 97.5%. Regarding the quantitative measurement of the damaged area, since the output mask is binarized, where the damage region is 1 and the background is 0, the damaged area can be measured by summing the pixels of the damaged mask. Innovative applications also involve the implementation of the developed models within web platforms directly accessible from mobile devices. In this regard, [PERSON] et al. [35] proposed an advanced model using the CNN deep learning algorithm to automatically detect two types of concrete historical building defects--cracks and spalls--processing images acquired with different resolutions and sizes from mobile devices, digital cameras, and the web. According to binary classification, the model classified images into two categories: cracks (0) and spalls (1), achieving high accuracy (98.25% for training, 97.73% for validation) with training on diverse concrete surface images. This approach was proven to be robust against noise interference and holds potential for widespread application in defect detection. The architecture is implemented in a cloud web application designed to improve automatic real-time defect identification using mobile devices.