## 4 Conclusions  
This study initially examined the heterogeneity in damaged buildings following an earthquake. Additionally, satellite remote sensing and UAV datasets from the Afghanistan and Baoxing earthquakes were compiled, classifying building damage into four categories as follows: intact, slightly damaged, severely damaged, and collapsed. To address the challenge of pixel-level assessment of building damage post-earthquake, a network named BDHE-Net was proposed to significantly enhance the model's accuracy in classifying severely damaged and collapsed buildings. The method was tested on our dataset and benchmarked against three state-of-the-art methods. Furthermore, the role of BDAM, MFAF, BDA, and combined loss function modules was explored. The experimental results show that the introduction of these four strategies improves the mean F1 and mean IOU value distribution by 3.41% and 3.22%, respectively, compared with the baseline model. This paper presents the following key contributions:  
1. A novel deep learning-based model is proposed to solve the pixel-level classification problem for post-earthquake building damage assessment, which is crucial for earthquake rescue and post-disaster damage assessment. 2. BDAM, MFAF, BDA, and combined loss function modules are incorporated into BDHE-Net, which enhance the model's capacity to discern varying levels of damage among buildings. In the future, we will attempt to use multi-modal images, which refer to the fusion of data acquired by different sensors, such as optical imagery, radar data, hyperspectral data, and so on. By fusing data from different modalities, more comprehensive and multi-angle information can be obtained, thus enhancing the accuracy of building damage assessment. For example, optical images can provide information on the shape and texture of a building, while radar data can penetrate clouds and smoke to obtain structural information about a building, and hyperspectral data can provide rich spectral features for distinguishing buildings made of different materials. Pixel-level assessment of building damage under complex conditions can be further investigated to address the requirements of emergency rescue and post-disaster reconstruction efforts. Conceptualization, J.L. and Y.L. ; methodology, Y.L. ; software, Y.L. ; validation, J.W., J.L., S.C., Y.W. and Y.L. ; formal analysis, J.W., Y.W. and Y.L. ; resources, J.W. and S.C.; data curation, J.L. and J.W. ; writing--original draft preparation, J.L., Y.L. and S.C.; writing--review and editing, J.L., Y.L. and S.C.; supervision, J.W., J.L., Y.W. and S.C. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Key R&D Program of China, grant number 2022 YFC3004405; the National Natural Science Foundation of China, grant number 42061073; and the Natural Science and Technology Foundation of Guizhou Province under Grant [2020]1Z056. **Informed Consent Statement:** Not applicable. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Methods**} & **Intact/F1** & \multicolumn{2}{c}{**Slightly**} & **Severely** & \multirow{2}{*}{**Collapsed/F1**} & \multirow{2}{*}{**Mean/F1**} & \multirow{2}{*}{**Mean/F1**} & \multirow{2}{*}{**Mean/P**} & \multirow{2}{*}{**Mean/R**} & \multirow{2}{*}{**Mean/IOU**} \\  & **(\%)** & & **Damaged/F1** & & **(\%)** & & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Baseline & 82.18 & 52.04 & 51.43 & 67.41 & 60.94 & 60.68 & 61.46 & 43.93 \\ Baseline + M & 83.34 & 52.24 & 54.51 & 66.35 & 61.97 & 65.23 & 58.92 & 44.84 \\ Baseline + M + B & 83.83 & 56.51 & 53.45 & 69.63 & 63.68 & 65.73 & 61.42 & 46.64 \\ Baseline + M + B & 83.62 & 53.31 & 56.63 & 70.95 & 64.35 & 64.36 & 63.73 & 47.15 \\ + D + C & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 5: The impact of various modules within the BDHE-Net architecture on the accuracy of building damage classification.