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经常成为系统性攻击目标。这是因为关键基础设施对于保障人员与物资的连通性和运输至关重要，从而支撑着国家和国际防御规划及经济增长。在自然和人为灾害期间，桥梁的大规模破坏以及对这些设施极低或完全不可达的状况，阻碍了我们实现快速恢复。因此，系统韧性急剧下降。解决这一挑战的方案是利用技术进行远距离观测。然而，目前尚无方法能够表征不同尺度（即区域级、资产级和结构（构件）级）的损伤，更不用说在不同尺度评估之间几乎没有系统性的关联。我们提出了一种集成的三级分层方法以填补这一能力空白，并展示了利用适用数字技术实现损伤表征的方法。随后，该方法被应用于乌克兰一个包含17座桥梁的案例研究并进行了验证。从宏观到微观，我们按尺度部署技术，从Sentinel-1 SAR图像、众包信息、高分辨率图像到用于受损基础设施的深度学习。首次利用干涉相干差和图像语义分割来提高从区域级到基础设施构件级损伤表征的可靠性，这在需要增强评估精度时尤为关键。这种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