Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
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DOI：
10.1145/3615884.3629422
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发表时间：
2023-10
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影响因子：
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通讯作者：
Chenguang Wang;Yepeng Liu;Xiaojian Zhang;Xuechun Li;Vladimir Paramygin;Arthriya Subgranon;Peter She
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文献类型：
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作者：
Chenguang Wang;Yepeng Liu;Xiaojian Zhang;Xuechun Li;Vladimir Paramygin;Arthriya Subgranon;Peter She
及时、准确地评估飓风造成的建筑损坏对于有效的飓风后应对和恢复工作至关重要。最近，遥感技术在灾难性事件发生后立即提供大规模光学或干涉合成孔径雷达（InSAR）图像数据，可轻松用于快速进行建筑物损坏评估。与光学卫星图像相比，合成孔径雷达可以穿透云层，并在各种天气条件下提供更完整的受损区域的空间覆盖。然而，这些 InSAR 图像通常包含由同时发生或位于同一地点的建筑物损坏、洪水、洪水/风引起的植被变化以及人为活动引起的高噪声和混合信号，使得提取准确的建筑物损坏信息具有挑战性。在本文中，我们介绍了一种基于因果关系的贝叶斯网络推理方法，用于根据 InSAR 图像快速检测飓风后建筑物损坏。该方法使用整体因果贝叶斯网络对风、洪水、建筑损坏和 InSAR 图像之间的复杂因果关系进行编码。基于因果贝叶斯网络，我们通过将 InSAR 图像信息与先前的洪水和风物理模型融合，进一步共同推断了大规模的未观测到的建筑物损坏，而无需地面实况标签。此外，我们在现实世界的毁灭性飓风——2022 年飓风伊恩中验证了我们的估计结果。我们收集并注释了佛罗里达州李县的建筑物损坏地面实况数据，并将引入的方法的估计结果与地面实况进行比较，并将其与最先进的模型进行基准测试，以评估我们提出的方法的有效性。结果表明，我们的方法可以快速准确地检测建筑物损坏，与传统的手动检查方法相比，处理时间显着缩短。
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event, which can be readily used to conduct rapid building damage assessment. Compared to optical satellite imageries, the Synthetic Aperture Radar can penetrate cloud cover and provide more complete spatial cover-age of damaged zones in various weather conditions. However, these InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities, making it challenging to extract accurate building damage information. In this paper, we introduced a causality-informed Bayesian network inference approach for rapid post-hurricane building damage detection from InSAR imagery. This approach encoded complex causal dependencies among wind, flood, building damage, and InSAR imagery using a holistic causal Bayesian network. Based on the causal Bayesian network, we further jointly inferred the large-scale unobserved building damage by fusing the information from InSAR imagery with prior physical models of flood and wind, without the need for ground truth labels. Furthermore, we validated our estimation results in a real-world devastating hurricane---the 2022 Hurricane Ian. We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method. Results show that our method achieves rapid and accurate detection of building damage, with significantly reduced processing time compared to traditional manual inspection methods.