用Mamba模型融合爆炸力与遥感图像，快速评估爆炸后建筑损伤。
A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessment
- 基于Mamba的多模态网络，融合多尺度爆炸载荷与遥感图像
- 在贝鲁特爆炸事件数据上优于现有方法，实现更精准损伤识别
- 适合灾害应急响应、城市安全监测等实时评估场景
准确快速的结构损伤评估（SDA）对灾后管理至关重要，有助于救援人员优先分配资源、制定搜救计划并支持恢复工作。传统现场勘查虽精确，但受限于可达性、安全风险和时间约束，尤其在大型爆炸后难以实施。基于遥感的机器学习方法成为可扩展的快速SDA解决方案，其中基于Mamba的网络已达到最先进水平。然而，这些方法通常需要大量训练数据和长时间训练，限制了实际应用；且未能融入爆炸载荷的关键物理特征。为此，我们提出一种基于Mamba的多模态网络，将多尺度爆炸载荷信息与光学遥感图像相结合，用于快速结构损伤评估。在2020年贝鲁特爆炸事件数据集上，该方法显著优于现有先进方法。代码已公开于：https://github.com/IMPACTSquad/Blast-Mamba
原文摘要 · Abstract (English)
Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, and support recovery. Traditional field inspections, though precise, are limited by accessibility, safety risks, and time constraints, especially after large explosions. Machine learning with remote sensing has emerged as a scalable solution for rapid SDA, with Mamba-based networks achieving state-of-the-art performance. However, these methods often require extensive training and large datasets, limiting real-world applicability. Moreover, they fail to incorporate key physical characteristics of blast loading for SDA. To overcome these challenges, we propose a Mamba-based multimodal network for rapid SDA that integrates multi-scale blast-loading information with optical remote sensing images. Evaluated on the 2020 Beirut explosion, our method significantly improves performance over state-of-the-art approaches. Code is available at: https://github.com/IMPACTSquad/Blast-Mamba
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