用领域自适应提升灾后建筑损毁检测的跨区域可靠性
Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation
- 采用两阶段集成方法，通过有监督领域自适应迁移模型
- 在未见数据上实现0.5552的宏平均F1，无领域自适应则完全失效
- 适合需要跨灾害、跨地区部署的灾情评估系统使用
从遥感影像中快速进行结构损毁评估对及时响应灾害至关重要。在人机协同灾害管理系统中，自动化损毁检测为决策者提供可操作的情境感知。然而，基于多灾害基准训练的模型在未见地理区域表现不佳，源于训练与部署数据之间的分布差异，削弱了人类对自动化评估的信任。本文探索一种两阶段集成方法，利用有监督领域自适应（SDA）实现四个损毁等级的建筑损毁分类。该流程将xView2第一名方法适配至Ida-BD数据集，系统研究各类增强组件对分类性能的影响。在未见的Ida-BD测试集上的全面消融实验表明，领域自适应不可或缺：移除后损毁检测完全失败。采用未锐化增强的RGB输入时，该流程达到最鲁棒性能，宏平均F1为0.5552。结果凸显了领域自适应在构建可信自动化损毁评估模块中的关键作用。
原文摘要 · Abstract (English)
Rapid structural damage assessment from remote sensing imagery is essential for timely disaster response. Within human-machine systems (HMS) for disaster management, automated damage detection provides decision-makers with actionable situational awareness. However, models trained on multi-disaster benchmarks often underperform in unseen geographic regions due to domain shift - a distributional mismatch between training and deployment data that undermines human trust in automated assessments. We explore a two-stage ensemble approach using supervised domain adaptation (SDA) for building damage classification across four severity classes. The pipeline adapts the xView2 first-place method to the Ida-BD dataset using SDA and systematically investigates the effect of individual augmentation components on classification performance. Comprehensive ablation experiments on the unseen Ida-BD test split demonstrate that SDA is indispensable: removing it causes damage detection to fail entirely. Our pipeline achieves the most robust performance using SDA with unsharp-enhanced RGB input, attaining a Macro-F1 of 0.5552. These results underscore the critical role of domain adaptation in building trustworthy automated damage assessment modules for HMS-integrated disaster response.
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