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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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FullText.pdf | Copyright © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( https://creativecommons.org/licenses/by/4.0/ ). | 28.69 MB | Adobe PDF | View/Open |
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