提升灾后建筑损毁评估模型在不同灾害类型间的泛化能力
Improved MambdaBDA Framework for Robust Building Damage Assessment Across Disaster Domains
- 引入焦点损失、注意力门和对齐模块缓解类别不平衡与背景干扰
- 在多个灾害数据集上实现0.8%至27%的性能提升，跨灾害测试增益达27%
- 适合需要高泛化能力的灾后评估系统研发者使用
从卫星影像中可靠进行灾后建筑损毁评估（BDA）面临严重类别不平衡、背景杂乱以及灾害类型与地理区域间域偏移等问题。本文针对ChangeMamba架构中的MambaBDA模型进行改进，引入三个模块化组件：(i) 焦点损失以缓解类别不平衡问题；(ii) 轻量级注意力门抑制无关上下文；(iii) 紧凑对齐模块在解码前将灾前特征空间对齐至灾后内容。实验在xbd、巴基斯坦洪灾、土耳其地震和伊达飓风等多个卫星影像数据集上进行，涵盖同域与跨数据集测试。所提增强方法在同域下取得0.8%至5%的性能提升，跨灾害测试最高达27%增益，表明其显著增强了模型的泛化能力。
原文摘要 · Abstract (English)
Reliable post-disaster building damage assessment (BDA) from satellite imagery is hindered by severe class imbalance, background clutter, and domain shift across disaster types and geographies. In this work, we address these problems and explore ways to improve the MambaBDA, the BDA network of ChangeMamba architecture, one of the most successful BDA models. The approach enhances the MambaBDA with three modular components: (i) Focal Loss to mitigate class imbalance damage classification, (ii) lightweight Attention Gates to suppress irrelevant context, and (iii) a compact Alignment Module to spatially warp pre-event features toward post-event content before decoding. We experiment on multiple satellite imagery datasets, including xBD, Pakistan Flooding, Turkey Earthquake, and Ida Hurricane, and conduct in-domain and crossdataset tests. The proposed modular enhancements yield consistent improvements over the baseline model, with 0.8% to 5% performance gains in-domain, and up to 27% on unseen disasters. This indicates that the proposed enhancements are especially beneficial for the generalization capability of the system.
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