Rapid and accurate assessment of affected areas is crucial for post-earthquake rescue efforts, as earthquakes can lead to extensive damage and casualties. The post-earthquake damage assessment method based on SAR coherence is widely utilized, but issues such as inadequate consideration of decorrelation factors and underutilization of preseismic coherence can negatively impact assessment outcomes. To address these limitations and enhance accuracy while reducing false alarms, we propose a novel approach for post-earthquake building damage assessment utilizing a SAR coherence time matrix. The proposed method involves constructing time matrices by computing preseismic image coherence to maximize the utilization of preseismic coherence information. By developing a Vision Transformer model within the realm of deep learning, we aimed to extract features from these time matrices based on their unique characteristics. Through the use of predicted values obtained from the trained model to simulate coseismic coherence, a scoring metric was established as a proxy for damage. This novel method was successfully applied to evaluate the damage caused by the 2016 Italy earthquake and the 2023 Turkey earthquake, yielding improved accuracy and reduced false alarm rates. The research findings demonstrate the transferability and reliability of this method, presenting it as an accurate and dependable tool for post-earthquake building damage assessment.

中文翻译：

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先进的震后建筑损坏评估：带有 Vision Transformer 的 SAR 相干时间矩阵


快速、准确地评估受影响地区对于震后救援工作至关重要，因为地震可能导致广泛的破坏和人员伤亡。基于SAR相干性的震后损害评估方法得到广泛应用，但对去相关因素考虑不足、震前相干性利用不足等问题会对评估结果产生负面影响。为了解决这些限制并提高准确性，同时减少误报，我们提出了一种利用 SAR 相干时间矩阵进行震后建筑损坏评估的新方法。该方法涉及通过计算震前图像相干性来构造时间矩阵，以最大限度地利用震前相干性信息。通过在深度学习领域开发 Vision Transformer 模型，我们的目标是根据这些时间矩阵的独特特征从这些时间矩阵中提取特征。通过使用从训练模型获得的预测值来模拟同震相干性，建立了一个评分指标作为损坏的代理。这种新颖的方法已成功应用于评估 2016 年意大利地震和 2023 年土耳其地震造成的损失，提高了准确性并降低了误报率。研究结果证明了该方法的可移植性和可靠性，使其成为震后建筑损坏评估的准确可靠的工具。