DamageCAT:用于基于类型学的灾后建筑损坏分类的深度学习转换框架

IF 4.5 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Yiming Xiao, Ali Mostafavi
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引用次数: 0

摘要

快速、准确和描述性的建筑损坏评估对于指导灾后资源至关重要,然而目前的自动化方法通常只提供二进制(损坏/未损坏)或顺序严重性尺度。本文介绍了一个通过基于类型的分类分类来推进损伤评估的框架——DamageCAT。我们贡献了:(1)BD-TypoSAT数据集,其中包含飓风Ida的卫星图像三组,其中包含四种损坏类别——部分屋顶损坏、全部屋顶损坏、部分结构倒塌和全部结构倒塌;(2)基于u - net的分层变压器架构,用于处理灾前和灾后图像对。我们的模型总体上获得了0.737 IoU和0.846 f1得分,通过跨事件评估证明了飓风哈维、佛罗伦萨和迈克尔数据的可转移性。尽管由于等级不平衡,不同损害类别的性能有所不同,但该框架表明,与传统的基于严重程度的方法相比,基于类型学的分类可以提供更具可操作性的损害评估,从而实现有针对性的应急响应和资源分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DamageCAT: A deep learning transformer framework for typology-based post-disaster building damage categorization
Rapid, accurate, and descriptive building damage assessment is critical for directing post-disaster resources, yet current automated methods typically provide only binary (damaged/ undamaged) or ordinal severity scales. This paper introduces DamageCAT, a framework that advances damage assessment through typology-based categorical classifications. We contribute: (1) the BD-TypoSAT dataset containing satellite image triplets from Hurricane Ida with four damage categories – partial roof damage, total roof damage, partial structural collapse, and total structural collapse – and (2) a hierarchical U-Net-based transformer architecture for processing pre- and post-disaster image pairs. Our model achieves 0.737 IoU and 0.846 F1-score overall, with cross-event evaluation demonstrating transferability across Hurricane Harvey, Florence, and Michael data. While performance varies across damage categories due to class imbalance, the framework shows that typology-based classification can provide more actionable damage assessments than traditional severity-based approaches, enabling targeted emergency response and resource allocation.
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来源期刊
International journal of disaster risk reduction
International journal of disaster risk reduction GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
8.70
自引率
18.00%
发文量
688
审稿时长
79 days
期刊介绍: The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international. Key topics:- -multifaceted disaster and cascading disasters -the development of disaster risk reduction strategies and techniques -discussion and development of effective warning and educational systems for risk management at all levels -disasters associated with climate change -vulnerability analysis and vulnerability trends -emerging risks -resilience against disasters. The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.
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