Advanced Post-earthquake Building Damage Assessment: SAR Coherence Time Matrix with Vision Transformer

• Coherence Time Matrix: Constructing coherence time matrices to maximize the utilization of pre-earthquake coherence information. • SAR Meets Deep Learning: Applying Vision Transformer in building damage assessment aids in feature learning and information extraction from coherence data. • Timeliness: Rapid and accurate earthquake assessment results can be achieved by analyzing historical pre-earthquake data and only one post-earthquake image. 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.