# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Post-Disaster Damage at District Scale
#### 3.3.2 Multi-Class Classification  
An additional analysis conducted on images related to post-disaster damage situations focused on distinguishing the severity of the detected damage. Following a multi-class approach, DL-based models can assign new images to their respective severity classes. Moreover, some studies have sought methods to overcome the lack of post-disaster data by classifying new data using training conducted on historical data related to events and areas with different characteristics. Among these, [PERSON] et al. [54] explored a different approach to the early detection of post-disaster building damage. In their study, a novel Domain Adaptation (DA) framework was developed. This method aimed to adapt a model learned from a dataset related to a specific context (source domain) to perform in a different context (target domain), where data may have different characteristics. To validate the effectiveness of the proposed method, two damage detection tasks were studied using post-earthquake and post-hurricane datasets. Satellite imagery related to the Haiti disaster constituted the source domain, while the target domain included imagery from the Yushu earthquake. The method was further validated on datasets from post-hurricane Sandy (source domain) and Irma (target domain). In both cases, the samples were classified into three categories: undamaged, damaged, and other. The model aimed to achieve category-level feature fitting for complex image classification tasks by combining the variational autoencoder (VAE) and the Gaussian mixture model (GMM). Specifically, the GMM was used to characterize the distribution of the source domain, while the VAE estimated the distribution of the target domain, aligning each category feature with the corresponding category of the source domain by minimizing the KL divergence between the two domains. The experimental results revealed good performance of the model, reaching an accuracy value of 78.2% for post-earthquake images and 94.3% for post-hurricane images. Similarly, [PERSON] et al. [55] addressed the challenge of transferring models trained on historical datasets to novel data related to different contexts through transfer learning approaches. For this purpose, based on post-earthquake building damage data (Ludian and Yushu earthquakes), remote sensing images (aerial and UAV images) were labeled according to four damage classes (No observable damage, Light damage, Heavy damage, and Collapse) by examining building characteristics, including contour, geometry, texture, and relationship with the surrounding area. To assess the seismic damage class of buildings, the proposed DL model architecture consisted of a CNN feature extractor, the VGG network, and an ordered regression classifier. The VGG-OR network was employed to develop a model trained on the Ludian dataset. Subsequently, four schemes were designed and compared to evaluate the performance of different transfer learning programs. Among them, a novel data transfer algorithm was proposed to identify potential historical data samples that are advantageous for the new dataset. This algorithm was used in conjunction with the training samples of the Yushu dataset. The results showed that when the training set of the new task reached about 10% of the historical data, the model achieved an overall accuracy of about 74%, improving the accuracy of seismic building damage assessment in data-limited contexts and making it applicable to new disaster scenarios. Instead, the research conducted by [PERSON] and [PERSON] [56] addressed the need for accurate analysis of the impact of natural disasters on infrastructure, including historic buildings, using advanced technologies such as CNNs and data fusion. The aim was to compare aerial images acquired pre- and post-disaster, specifically Hurricane Irma.