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

通过应用先进的技术和方法，本研究提出了一个综合三级分层方法来评估和描述关键基础设施的破坏程度，并在乌克兰的桥梁案例中进行了验证。通过在不同尺度上应用技术，从区域到基础设施的组成部分，我们提高了评估的准确性，从而提高了决策速度，增强了恢复和应急能力。