Critical infrastructure such as bridges are systematically targeted during wars and conflicts. This is because critical infrastructure is vital for enabling connectivity and transportation of people and goods, and hence, underpinning the national and international defence planning and economic growth. Mass destruction of bridges, along with minimal or no accessibility to these assets during natural and anthropogenic disasters, prevents us from delivering rapid recovery. As a result, systemic resilience is drastically reduced. A solution to this challenge is to use technology for stand-off observations. Yet, no method exists to characterise damage at different scales, i.e. regional, asset, and structural (component), and more so there is little or no systematic correlation between assessments at scale. We propose an integrated three-level tiered approach to fill this capability gap, and we demonstrate the methods for damage characterisation enabled by fit-for-purpose digital technologies. Next, this method is applied and validated to a case study in Ukraine that includes 17 bridges. From macro to micro, we deploy technology at scale, from Sentinel-1 SAR images, crowdsourced information, and high-resolution images to deep learning for damaged infrastructure. For the first time, the interferometric coherence difference and semantic segmentation of images were deployed to improve the reliability of damage characterisations from regional to infrastructure component level, when enhanced assessment accuracy is required. This integrated method improves the speed of decision-making, and thus, enhances resilience. Keywords: critical infrastructure, damage characterisation, targeted attacks, restoration

翻译：桥梁等关键基础设施在战争和冲突中常成为系统性打击目标。这是因为关键基础设施对于保障人员与物资的连通运输至关重要，因而支撑着国家及国际防务规划与经济增长。在自然与人为灾害期间，桥梁大规模损毁且难以或无法抵达，阻碍了快速恢复的开展，导致系统韧性急剧下降。解决这一挑战的方案是利用技术进行非接触观测。然而，目前尚不存在能够表征不同尺度（即区域级、资产级和结构/组件级）损伤的方法，更遑论不同尺度评估之间的系统性关联。我们提出了一种三级集成分层方法来填补这一能力空白，并展示了借助适用数字技术实现的损伤表征方法。随后，该方法在乌克兰的17座桥梁案例研究中得到应用与验证。从宏观到微观，我们按尺度部署技术：从Sentinel-1合成孔径雷达影像、众源信息及高分辨率影像，到用于受损基础设施的深度学习。首次引入干涉相干性差异与图像语义分割技术，在需要提升评估精度时，可从区域级到基础设施组件级提高损伤表征的可靠性。这一集成方法提升了决策速度，从而增强了韧性。关键词：关键基础设施，损伤表征，针对性攻击，修复