Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery. Traditional methods, which often rely on manual inspections or sparse data, are typically slow and error-prone. This paper introduces a novel framework leveraging remote sensing imagery and deep learning to automate building damage classification. Using pre- and post-disaster satellite imagery, our model categorizes buildings into four damage levels: no damage, minor damage, major damage, and destroyed. The core innovation is a multi-modal attention mechanism that fuses bi-temporal features to explicitly detect and assess structural changes. We employ a lightweight ConvNeXT-Tiny backbone to ensure efficient processing without compromising performance. Key contributions include: (1) a cross-attention module for multi-modal data fusion, (2) an optimized preprocessing pipeline for large-scale datasets, and (3) robust data augmentation techniques. Experiments on a large-scale disaster dataset demonstrate an overall classification accuracy of 94.90%. The model effectively discriminates between damage categories and remains resilient to incomplete data. This system significantly improves assessment speed and accuracy, aiding emergency responders in prioritizing interventions. This work advances automated disaster damage detection by integrating multi-temporal imagery with deep learning, offering a scalable solution for real-time response.

翻译：及时准确的灾害损伤评估对高效应急响应、资源分配及灾后重建至关重要。传统方法通常依赖人工巡检或稀疏数据，过程缓慢且易出错。本文提出一种利用遥感图像与深度学习实现建筑损伤自动分类的新型框架。基于灾前与灾后卫星图像，模型将建筑损伤划分为四个等级：无损伤、轻度损伤、重度损伤和完全损毁。核心创新在于提出多模态注意力机制，融合双时相特征以显式检测与评估结构变化。我们采用轻量级ConvNeXT-Tiny骨干网络，在保证处理效率的同时不牺牲性能。主要贡献包括：(1) 面向多模态数据融合的交叉注意力模块，(2) 针对大规模数据集的优化预处理流程，(3) 强鲁棒性数据增强技术。在大规模灾害数据集上的实验表明，整体分类准确率达到94.90%。该模型能有效区分不同损伤类别，并对数据缺失场景保持鲁棒性。本系统显著提升了评估速度与精度，有助于应急人员优先排序干预措施。本研究通过整合多时相图像与深度学习推进自动化灾害损伤检测，为实时响应提供了可扩展的解决方案。