Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach

Abstract

Critical infrastructure is vital for connectivity and economic growth but faces systemic threats from human-induced damage, climate change and natural disasters. Rapid, multi-scale damage assessments are essential, yet integrated, automated methodologies remain underdeveloped. This paper presents a multi-scale tiered approach, which addresses this gap, by demonstrating how automated damage characterisation can be achieved using digital technologies. The methodology is then applied and validated through a case study in Ukraine involving 17 bridges damaged by targeted human interventions. Technology is deployed across regional to component scales, integrating assessments using Sentinel-1 SAR images, crowdsourced data, and high-resolution images for deep learning to enable automatic damage detection and characterisation. The interferometric coherence difference and semantic segmentation of images are utilised in a tiered multi-scale approach to enhance the reliability of damage characterisation at various scales. This integrated methodology automates and accelerates decision-making, facilitating more efficient restoration and adaptation efforts and ultimately enhancing infrastructure resilience.