Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach

Critical infrastructure are systematically targeted during wars and extensive natural disasters because critical infrastructure is vital for enabling connectivity and transportation of people and goods, and hence, underpins national and international economic growth. Mass destruction of transport assets, in conjunction with minimal or no accessibility in the wake of natural and anthropogenic disasters, prevents us from delivering rapid recovery and adaptation. A solution to this challenge is to use technology that enables stand-off observations. Nevertheless, no methods exist for the integrated characterisation of damage at multiple scales, i.e. regional, asset, and structural scales, while there is no systematic correlation between infrastructure damage assessments across these scales. We propose a methodology based on an integrated multi-scale tiered approach to fill this capability gap. In doing so, we demonstrate how damage characterisation can be enabled by fit-for-purpose digital technologies. Next, the methodology is applied and validated to a case study in Ukraine that includes 17 bridges all damages by human targeted interventions. From macro to micro, we deploy technology to integrate assessments at scale, using from Sentinel-1 SAR images, crowdsourced information, and high-resolution images to deep learning to characterise infrastructure damage. For the first time, the interferometric coherence difference and semantic segmentation of images were deployed to improve the reliability of damage characterisations at different scales, i.e. regional, infrastructure asset and component, with the aim of enhancing the damage characterisation accuracy. This integrated approach accelerates decision-making, and therefore, facilitates more efficient restoration and adaptation efforts, ultimately fostering resilience into our infrastructure.