Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake

Foulser‐Piggott, Roxane; Spence, Robin; Eguchi, R. T.; King, Andrew

doi:10.1193/051214eqs067m

Cited by 18 publications

(19 citation statements)

References 4 publications

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“…Estimating damage from remote sensing data, such as satellite photography, has been widely performed recently (Booth et al 2011; Foulser-Piggott et al 2016). Comparing the damage levels found in the field survey and photo analysis showed that photo analysis is a reasonable method to identify story-collapsed buildings (D5), but it is difficult to identify D4 buildings (tilted buildings without story collapse).…”

Section: Discussionmentioning

confidence: 99%

Wooden Building Damage Analysis in Mashiki Town for the 2016 Kumamoto Earthquakes on April 14 and 16

2017

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The 2016 Kumamoto earthquakes caused serious building damage in Mashiki town. Because two large earthquakes occurred within an interval of 28 hours, it is difficult to separate the damage caused by each of these earthquakes. We analyzed aerial photos of the center of Mashiki town taken before and after the second event, which allow us to separate the damage caused by the two earthquakes. Our analysis shows that building damage was concentrated especially on the river terrace of the Akitsu river, and there were almost no collapsed buildings in the south of the damaged area. The pattern of damage distribution of the two events was similar, which suggests that the damage to the wooden buildings was caused by local conditions. The analysis of past aerial photos showed that the heterogeneity of the damage distribution is difficult to explain by only the building age. The cause of this heterogeneity was found to be not because of an earthquake faulting effect, but because of a combination of building seismic performance and local site conditions.

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Section: Discussionmentioning

confidence: 99%

Wooden Building Damage Analysis in Mashiki Town for the 2016 Kumamoto Earthquakes on April 14 and 16

2017

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“…The use of imaging technology has been increasing rapidly as data gathering, and enriching tool boosting mission capabilities and ensure their safe deployment in areas affected by earthquakes [35]. For about the last decade, high-resolution optical satellite and aerial images have been increasingly used for a rapid building-damage survey after an earthquake [25]; they have become an important tool for rapid and reliable structural damage assessment (SDA) [22]. We identified in our literature review six RS data sources used for earthquake reconnaissance.…”

Section: Remote Sensing (Rs)mentioning

confidence: 99%

“…The result of the damage assessment will be the aggregate quantities of damages for an exposure unit. This quantitative assessment is used to assess the direct economic loss as the basis for calculating the value of economic losses: the replacement cost [19] or insurance payouts of value to international aid organisation, bi-lateral/multilateral donors and the insurance industry [25]. During the emergency or relief phase, the quantitative assessment of damage starts with the structural component of the buildings due to its essential role in the safety of the population affected by earthquakes and the estimation of temporary shelter needed [9].…”

Section: Introductionmentioning

confidence: 99%

Earthquakes Reconnaissance Data Sources, a Literature Review

Mojica¹,

Wilkinson²,

James³

2021

Preprint

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Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and guide more efficient data. This paper reviews 38 articles that indicate the sources used by different authors to collect data related to damages and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria. Nowadays, reconnaissance mission can not rely on a single data source, and different data sources should complement each other, validate collected data, or quantify the damage comprehensively. The recent increase in the number of crowdsourcing and SM platforms as a source of data for earthquake reconnaissance is a clear indication of the tendency of data sources in the future.

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“…Nevertheless, identifying noncollapsed damage states is a more challenging task. For instance, Foulser-Piggott et al (2016) attempted to identify the red-tagged or yellow-tagged noncollapsed buildings from the 2011 Christchurch earthquake, and the omission error was found to be 56%-86%. Note that currently there is no feasible solution to effectively reduce such an error.…”

Section: Identification Of Collapsed Buildingsmentioning

confidence: 99%

Improving the Accuracy of near Real-Time Seismic Loss Estimation using Post-Earthquake Remote Sensing Images

Zeng

et al. 2018

Earthquake Spectra

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With the rapid development of remote sensing technology, satellite or aerial images from the disaster area become available within 24 hours after an earthquake. The collapsed buildings can be easily identified from these images. In this work, a framework for near real-time seismic loss estimation for regional buildings is proposed, which improves the accuracy of nonlinear time-history analysis (THA)-based loss estimations by taking advantage of the identified building collapse scene of the disaster area. Specifically, a series of THA are performed for the target regional buildings, thereby generating a number of simulation results. Those simulation results that bear strong similarities to the identified collapse scene are identified as the optimal solutions, which will be used to estimate the seismic loss. The simulation results of the case studies signify that the use of the identified building collapse scene leads to much closer estimations to actual economic losses.

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Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake

Cited by 18 publications

References 4 publications

Wooden Building Damage Analysis in Mashiki Town for the 2016 Kumamoto Earthquakes on April 14 and 16

Wooden Building Damage Analysis in Mashiki Town for the 2016 Kumamoto Earthquakes on April 14 and 16

Earthquakes Reconnaissance Data Sources, a Literature Review

Improving the Accuracy of near Real-Time Seismic Loss Estimation using Post-Earthquake Remote Sensing Images

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