Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30455
Title: Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach
Authors: Kopiika, N
Karavias, A
Krassakis, P
Ye, Z
Ninic, J
Shakhovska, N
Argyroudis, S
Mitoulis, S-A
Keywords: critical infrastructure;automatic damage detection;damage characterisation;multi-scale;targeted attacks;resilience;remote sensing;deep learning
Issue Date: 3-Jan-2025
Publisher: Elsevier
Citation: Kopiika, N. et al. (2025) 'Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach', Automation in Construction, 170, 105955, pp. 1 - 27. doi: 10.1016/j.autcon.2024.105955.
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.
Description: Data availability: No data was used for the research described in the article.
URI: https://bura.brunel.ac.uk/handle/2438/30455
DOI: https://doi.org/10.1016/j.autcon.2024.105955
ISSN: 0926-5805
Other Identifiers: ORCiD: Sotirios Argyroudis https://orcid.org/0000-0002-8131-3038
105955
Appears in Collections:Dept of Civil and Environmental Engineering Research Papers

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