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Rapid post-disaster infrastructure damage characterisation using remote sensing and deep learning technologies: A tiered approach
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-03 , DOI: 10.1016/j.autcon.2024.105955
Nadiia Kopiika, Andreas Karavias, Pavlos Krassakis, Zehao Ye, Jelena Ninic, Nataliya Shakhovska, Sotirios Argyroudis, Stergios-Aristoteles Mitoulis
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-03 , DOI: 10.1016/j.autcon.2024.105955
Nadiia Kopiika, Andreas Karavias, Pavlos Krassakis, Zehao Ye, Jelena Ninic, Nataliya Shakhovska, Sotirios Argyroudis, Stergios-Aristoteles Mitoulis
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.
中文翻译:
使用遥感和深度学习技术快速描述灾后基础设施损坏特征:一种分层方法
关键基础设施对于连接和经济增长至关重要,但面临人为破坏、气候变化和自然灾害的系统性威胁。快速、多尺度的损失评估是必不可少的,但集成的自动化方法仍然不发达。本文提出了一种多尺度分层方法,通过展示如何使用数字技术实现自动损伤表征来解决这一差距。然后,该方法通过乌克兰的一个案例研究进行应用和验证,该案例研究涉及 17 座被有针对性的人工干预损坏的桥梁。该技术从区域到组件规模进行部署,使用 Sentinel-1 SAR 图像、众包数据和高分辨率图像集成评估以进行深度学习,以实现自动损伤检测和表征。图像的干涉相干差异和语义分割用于分层多尺度方法,以提高各种尺度损伤表征的可靠性。这种集成方法可以自动化并加速决策,促进更高效的恢复和适应工作,并最终增强基础设施的弹性。
更新日期:2025-01-03
中文翻译:
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使用遥感和深度学习技术快速描述灾后基础设施损坏特征:一种分层方法
关键基础设施对于连接和经济增长至关重要,但面临人为破坏、气候变化和自然灾害的系统性威胁。快速、多尺度的损失评估是必不可少的,但集成的自动化方法仍然不发达。本文提出了一种多尺度分层方法,通过展示如何使用数字技术实现自动损伤表征来解决这一差距。然后,该方法通过乌克兰的一个案例研究进行应用和验证,该案例研究涉及 17 座被有针对性的人工干预损坏的桥梁。该技术从区域到组件规模进行部署,使用 Sentinel-1 SAR 图像、众包数据和高分辨率图像集成评估以进行深度学习,以实现自动损伤检测和表征。图像的干涉相干差异和语义分割用于分层多尺度方法,以提高各种尺度损伤表征的可靠性。这种集成方法可以自动化并加速决策,促进更高效的恢复和适应工作,并最终增强基础设施的弹性。