###### Abstract  
Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19-8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model's classification accuracy for severely damaged and collapsed categories can be improved. building damage assessment; deep learning; building damage heterogeneity; UAV and remote sensing data 2  
operating costs. Consequently, the utilization of remote sensing imagery and UAV imagery has become prevalent in studies pertaining to building damage assessment. The conventional approaches for building damage assessment include visual interpretation and field investigation, which are highly accurate but time-consuming and labor-intensive, especially when the affected area is large [2]. Change detection, based on pre- and post-earthquake remote sensing images, is also an effective approach to assessing building damage. [PERSON] [3] used an object-oriented classification method to extract building images before and after an earthquake and used a change detection method to analyze changes in the buildings. Although this method makes full use of multi-temporal features and obtains better accuracy, it requires pre-disaster images corresponding to the same location at the same time. In addition, this method can usually only extract images of collapsed buildings and cannot meet the need for a refined classification of building damage. Although this method fully utilizes multi-temporal features of the images, it cannot determine the damage level of buildings. The progress in deep learning technologies has led to the development of various neural network models, including recurrent neural networks (RNNs) [4], convolutional neural networks (CNNs) [5], and graph neural networks (GNNs) [6]. In particular, CNNs have shown promising potential in image classification and semantic segmentation. Therefore, they have been widely used in building damage assessment research. For example, [PERSON] [7] used three different CNN-based feature fusion methods based on residual connections and dilated convolutions to assess building damage at different resolutions. The results showed that when multiple-resolution feature maps were fused, and the feature information from intermediate layers of each resolution-level network was considered, better accuracy and localization capability were demonstrated. [PERSON] [8] developed RescueNet, a high-resolution dataset tailored for natural disaster analysis.