Evaluation of Building Damage and Tsunami Inundation Based on Satellite Images and GIS Data following the 2010 Chile Earthquake

Maruyama, Yoshihiro; Yamazaki, Fumio; Matsuzaki, Shizuko; Miura, Hiroyuki; Estrada, Miguel

doi:10.1193/1.4000023

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…Documenting the direct effects has become more sophisticated with the availability of various types of sensors and new technologies, but also more tedious and complicated (e.g., Annunziato et al 2010;Maruyama et al 2012). For example, remote sensing products (RS; Satellite or other data such as drone) can be processed to give precise flood extent, degree of erosion and damage (e.g., Liew et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Does the future of tsunami intensity scales lie in past events?

Barberopoulou

Scheele

2015

Nat Hazards

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Post-disaster assessments for historical tsunami events ([25 years old) are either scarce or contain limited information. In this study, we assess ways to examine tsunami impacts by utilizing data from old events, but more importantly we examine how to best utilize information contained in tsunami historical databases, in order to provide meaningful products that describe the impact of the event. As such, a tsunami intensity scale was applied to two historical events that were observed in New Zealand (one local and one distant), in order to utilize the largest possible number of observations in our dataset. This is especially important for places like New Zealand where the tsunami historical record is short, going back only to the nineteenth century, and where instrument recordings are only available for the most recent events. We found that despite a number of challenges in using intensities-uncertainties partly due to limitations of historical event data-these data with the help of GIS tools can be used to produce hazard maps and offer an alternative way to exploit tsunami historical records. Most importantly, the assignment of intensities at each point of observation allows for the utilization of many more observations than if one depends on physical information alone, such as water heights. We hope these results may be used toward developing a well-defined methodology for hazard assessments and refine our knowledge for past tsunami events for which the tsunami sources are largely unknown, but also when physical quantities describing the tsunami (e.g., water height, flood depth, and run-up) are scarce.

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Section: Introductionmentioning

confidence: 99%

Does the future of tsunami intensity scales lie in past events?

Barberopoulou

Scheele

2015

Nat Hazards

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“…Remote sensing has contributed significantly to damage assessment and has proved to be an efficient technology for this purpose [5]. Several research groups from across the globe have systematically used this technology for precise damage and recovery assessment [17], risk management [18], disaster management and monitoring [5,7], structural damage assessment [8], and building damage mapping [19,20]. In order to evaluate the status of damage, disaster waste disposal, and its direct impact on the surrounding environment, a team composed of three members from Kyushu University departed for the region to conduct a field investigation on March 15 and 16, 2018, almost 4 months after the disaster.…”

Section: Introductionmentioning

confidence: 99%

Lessons learned from the Ezgeleh–Sarpol Zahab earthquake of November 2017: status of damage and disposal of disaster waste

Saffarzadeh

Shimaoka

Nakayama

et al. 2019

Waste Dispos. Sustain. Energy

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A massive earthquake of magnitude M w 7.3 shook Kermanshah Province in Western Iran along the Iraqi border on November 12, 2017. The epicenter of the earthquake was approximately 10 km southwest of Ezgeleh Town in Kermanshah Province. Field observations almost 4 months after the disaster indicated that the earthquake had caused tremendous damage to most structures in both urban and rural areas, and left an enormous amount of disaster waste. To investigate the status of the damage and disposal of the disaster waste, remote sensing was conducted using an unmanned aerial vehicle (drone). Through the capture of low-altitude images by drone and the generation of 3D models, the quantity of debris accumulated in a waste disposal facility near Sarpol Zahab was estimated at approximately 480,000 m 3 . A compositional analysis of the disaster waste was performed using an imaging technique. This revealed that the disaster waste was largely composed of concrete (39.6%), hollow brick (35.4%), and gypsum (21.2%) in the urban area, whereas soil was the dominant component (77.4%) in the rural area. The damage caused to most buildings was essentially due to their non-standard construction. The management of debris from the damaged buildings was a critical issue for the authorities, and the lack of preparedness was a serious drawback that consumed an enormous amount of time, budget, and workforce. A practical post-disaster preparedness plan would help the decision-makers and the public to manage the otherwise overwhelming nature of the post-disaster conditions in a more reasonable manner.

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“…With the recent advent of remote sensing technologies, the estimation of affected areas in the aftermath of a large-scale disaster has considerably improved [1][2][3][4][5]. Interest in the use of remote sensing in disaster management is well motivated because remote sensing products can cover a wide affected area; multiple sensors equipped on satellites are constantly recording the Earth's surface; and active sensors function independently of the time and climate.…”

Section: Introductionmentioning

confidence: 99%

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

et al. 2018

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Although supervised machine learning classification techniques have been successfully applied to detect collapsed buildings, there is still a major problem that few publications have addressed. The success of supervised machine learning strongly depends on the availability of training samples. Unfortunately, in the aftermath of a large-scale disaster, training samples become available only after several weeks or even months. However, following a disaster, information on the damage situation is one of the most important necessities for rapid search-and-rescue efforts and relief distribution. In this paper, a modification of the supervised machine learning classification technique called logistic regression is presented. Here, the training samples are replaced with probabilistic information, which is calculated from the spatial distribution of the hazard under consideration and one or more fragility functions. Such damage probabilities can be collected almost in real time for specific disasters such as earthquakes and/or tsunamis. We present the application of the proposed method to the 2011 Great East Japan Earthquake and Tsunami for collapsed building detection. The results show good agreement with a field survey performed by the Ministry of Land, Infrastructure, Transport and Tourism, with an overall accuracy of over 80%. Thus, the proposed method can significantly contribute to a rapid estimation of the number and locations of collapsed buildings.

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Evaluation of Building Damage and Tsunami Inundation Based on Satellite Images and GIS Data following the 2010 Chile Earthquake

Cited by 13 publications

References 9 publications

Does the future of tsunami intensity scales lie in past events?

Does the future of tsunami intensity scales lie in past events?

Lessons learned from the Ezgeleh–Sarpol Zahab earthquake of November 2017: status of damage and disposal of disaster waste

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

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