## 1 Introduction  
Earthquakes are one of the most serious natural disasters affecting humans. They cause many houses to be damaged and collapse, severely affecting the safety of both people and property. One of the key issues after an earthquake is the assessment of the damage of buildings. The results of the assessment can provide important information for disaster relief work. The timely and accurate assessment of damaged buildings is critical for rescues and consequential loss assessment. Traditionally, post-earthquake building damage is evaluated and counted via manual field surveys, but this method is often time-consuming and laborious. [PERSON] et al. used the QuickBird satellite image after the Ms6.8 earthquake on the Mediterranean coast of Algeria and classified damaged buildings into five grades using the visual interpretation image method [1]. However, atmospheric conditions, such as cloud cover, will affect the image quality and lead to inaccurate evaluation. With the development of artificial intelligence, machine-learning-related technologies have been gradually applied to the post-earthquake building damage assessment. [PERSON] et al. used remote sensing data before and after the earthquake through the decision tree method, in which the damaged buildings were divided into four grades [2]. The neural network of the genetic algorithm (GA) and the neural network composed of multi-layer perceptron (MLP) are used to predict the risk level of damage to reinforced-concrete (RC) structures [3; 4]. The method achieves detailed investigation and inspection of buildings before the earthquake, reducing the loss of life and property. SMART SKY EYE (smart building safety assessment system using UAV) evaluates building wall cracks by analyzing natural factors based on machine learning methods such as random forest and support vector machine (SVM) [5]. However, these generalization capabilities are poor and the performance of the model is affected when the study area changes. Compared with machine learning, convolutional neural networks (CNNs) based on deep learning have strong image processing abilities, strong feature learning and visual recognition abilities, and are widely used in building damage assessment. The dual-temporal methods use CNN to extract information on the characteristics of the images before and after the earthquake to determine the degree of damage to the building [6]. [PERSON] et al. used deep-learning-based automatic detection and classification methods to evaluate and classify the loss levels of buildings in Ludian earthquake aerial images [7]. However, these methods can only achieve good performance when there are few categories, which cannot meet the needs for post-earthquake housing damage assessment. [PERSON] et al. also combined machine learning and deep learning methods to evaluate five types of damage to buildings and improve the evaluation performance of damaged buildings using a combination of texture information from random forests and deep features extracted by CNN [8]. The above methods use two-dimensional semantic information to complete the damage assessment of the building, but they only contain damage information on one side of the building. Therefore, there is a big difference between the assessment results and the actual damage. On the contrary, three-dimensional semantic stereo information can provide structural features and height information of buildings. It is helpful to evaluate the damage grade of buildings after an earthquake. [PERSON] et al. extracted the damaged information of buildings based on the differences in elevation between images before and after the earthquake [9]. However, the applicability of this method is limited due to the difficulty in obtaining pre-disaster and post-disaster digital elevation models (DEM). On the contrary, the 3D model efficiently reconstructed using the UAV can describe more detailed information of walls, beams, columns, and roofs from multiple angles [10; 11]. [PERSON] et al.