Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies – a tiered approach
arxiv(2024)
摘要
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.
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