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Examples of collapsed buildings extracted using the Lidar data. The left column shows the photos taken after the mainshock by the authors. The middle column shows the Lidar data, where the blue points depict the BDSM and the red points the ADSM. The right column shows the elevation differences between the two DSMs.
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The 2016 Kumamoto earthquake sequence was triggered by an Mw 6.2 event at 21:26 on April 14. Approximately 28 hours later, at 1:25 on April 16, an Mw 7.0 event (the mainshock) followed. The epicenters of both events were located 10 near the residential area of Mashiki town and the region nearby. Due to very strong seismic ground motion, the earthqu...
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![Fig. 1. Estimation of debris width and road closure [4].](profile/Sonia-Giovinazzi-2/publication/330005182/figure/fig1/AS:802238054277121@1568279865760/Estimation-of-debris-width-and-road-closure-4_Q320.jpg)
Crisis produced by earthquake events are often dramatic for their severity and their impact on population. Damages may extend from buildings to Critical Infrastructures. Predicting the functionality of the latter after an event is relevant for the design of contingency plans, as availability of primary services empowers the action of first responde...
The 2016 Kumamoto earthquake sequence was triggered by an Mw 6.2
event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April,
an Mw 7.0 event (the mainshock) followed. The epicenters of both
events were located near the residential area of Mashiki and affected the
region nearby. Due to very strong seismic ground motion, the earthquak...
![Fig. 1. Estimation of debris width and road closure [4].](profile/Sonia-Giovinazzi-2/publication/335756942/figure/fig1/AS:802222388559875@1568276130064/Estimation-of-debris-width-and-road-closure-4_Q320.jpg)
Crisis produced by earthquake events are often dramatic for their severity and their impact on population. Damages may extend from buildings to Critical Infrastructures. Predicting the functionality of the latter after an event is relevant for the design of contingency plans, as availability of primary services empowers the action of first responde...
This study demonstrates the use of multi-temporal LiDAR data to extract collapsed buildings and to monitor their removal process in Minami-Aso village, Kumamoto prefecture, Japan, after the April 2016 Kumamoto earthquake. By taking the difference in digital surface models (DSMs) acquired at pre- and post-event times, collapsed buildings were extrac...
Citations
... LiDAR is an airborne or terrestrial remote sensing method that is capable of gathering detailed point cloud data that can provide high-quality distance measures of land topography and water conditions, as well as other features. Based on these measures, high-resolution digital elevation models can be developed, which can then be used for multiple risk assessment [38,39] and emergency management purposes [40,41]. LiDAR scanners are also able to gather information about the ground surface below the vegetation, which can be used for mapping or measuring geological features that can provide data for monitoring volcano growth and predicting eruption patterns [42]. ...
Smart technologies such as artificial intelligence, the Internet of Things, and other cyber-physical systems are often associated to Industry 4.0 given their potential for transforming current manufacturing and industrial practices. In particular, the significant potential of these technologies for increasing automation, improving communication and self-monitoring, and optimizing production overall for industries is well known. However, the influential power of these technologies is not bounded by these applications and has significant potential for fields such as disaster risk reduction and emergency management. In this context, the proposed chapter discusses several applications of digital technologies and innovations from Industry 4.0 in these fields, such as big data, the Internet of Things and machine learning techniques for big data analytics. Additionally, research and governance needs in this context are highlighted, as well as certain challenges to widespread and mainstream the reliable use of these technologies in disaster management.
... Un ejemplo de esto es el estudio que viene realizando el profesor Kevin W. Franke de la Universidad de Brigham Young University (BYU), por medio de UAV generaron modelos 3D de las ciudades más afectadas por los eventos sismos conocidos como Central Italy Earthquakes en Italia durante el año 2016, con lo cual lograron demostrar el alcance de los daños provocados por este fenómeno en ese mismo año [11]. Estudios similares de análisis 3D, contemplando los cambios ocurridos en la zona antes y después del terremoto, fueron realizados por [12], [13] y [14]. Otros autores como [15], [16] y [7] se han enfocado en detectar edificios colapsados de manera inmediata a través de análisis de imagen. ...
This work proposes a method of detecting collapsed buildings after an earthquake with low altitude aerial images, which are captured by an unmanned aerial vehicles (UAVs) and classified with a convolutional neural network (CNN). Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over a large area, the purpose of this investigation is to achieve higher precision with the low altitud images. This thesis shows the advantages of CNN in comparison with other types of neural networks according with image recognition. The UAV images have unique advantages, such as stronger flexibility and higher resolution. The building damage was classified into five levels according with the European Macroseismic Scale 1988 (EMS-98) [1], instead of a binary classification of only collapsed and non-collapsed buildings as has been investigated by multiple authors. This damaged building mapping must be essential for the fast emergency response in Colombia cities.
... Furthermore, the Lidar datasets were employed to identify collapsed buildings and the results were consistent with the field investigation data (Yamada et al., 2017). Further information on this matter can be found in (Moya et al., 2018). ...
39th Asian Conference on Remote Sensing (ACRS 2018): Remote Sensing Enabling Prosperity // Proceedings of a meeting held 15-19 October 2018, Kuala Lumpur, Malaysia. AARS – Curran
Associates, Inc, 2019. Vol.3, 1676-1687.
The automated building and other man-made feature extraction from LiDAR point clouds together with the relevant topography generation is one of the most challenging research and development goals for urban studies and city monitoring procedures. The majority of the relevant software packages intends to focus on one only subject area: either Building (and other man-made features) Extraction (BE), or Change Detection (CD), or DEM Generation (DEM-G). This paper presents the EOS LiDAR Tool-the ELiT multifunctional software, developed by this paper authors with assistance of few their colleagues at the EOS Data Analytics Сompany (https://eos.com). ELiT is both a separate web-based application (ELiT Server), and an integrated component of the cloud-based EOS Platform-as-a-Service software (ELiT Cloud). Our software provides the ability to extract urban features from raw LiDAR data. The ELiT Server software architectural structure consists of two key constituents: ElitCore and ELiT Viewer. The latter obtains building models visualized in three levels of detail (LOD) in Cesium3D. ElitCore is the structured set of algorithmic procedures that accomplishes the whole computing pipeline. Core algorithms are schematically considered in the text. Three key features of our software-BE, CD, and DEM-G-correspond to three sub-pages of the ELIT Homepage. Each of these functionalities has been already approbated and tested on both open-source, and commercial datasets. With coming software update we will get a chance to provide urban modelling procedures not only in a block, and district scopes, but in a whole city scope as well. Any alterations in the city spatial infrastructure and in its social economic attributes will be able to be traced then. Thus, any city settlement, or a set of cities, may be examined, modelled and analysed with this software tools as an urban system, what can hardly be overvalued with respect to municipal management purposes.
... They also demonstrated how the data helped in reducing the time taken by first responders to reach disaster sites. Similarly, Moya et al. [51] detected collapsed buildings after the earthquake that struck Kumamoto, Japan on 16 April 2016. They did so by using digital surface models (DSMs), taken before and after the earthquake during LiDAR flights. ...
Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.
... 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. ...
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.
... From all the comparisons, the most suitable threshold value of the average height reduction within a reduced building footprint was determined as 0.5 m [19]. Figure 9 illustrates the spatial distribution of collapsed buildings estimated using this threshold for the entire study area, in which a large number of collapsed buildings were observed. ...
... This conclusion was further validated by the result of field damage investigation. Figure 9. Map showing the distribution of collapsed buildings, shown as red polygons, in the entire study area [19]. The pixel color represents the difference in elevations between the two DSMs (a). ...
Afetler, birçok insanın hayatını kaybetmesine, yaralanmasına, kitlesel göçlere ve ciddi ekonomik kayıplara neden olan olaylardır. Afetler nedeniyle meyda-na gelen kayıpların en aza indirilmesi için günümüzde; geçmişte kabul gören yara sarma politikası odaklı kriz yönetimi, yerini risk yönetiminin önemsendiği bütünleşik afet yönetimi sistemine bırakmıştır. Bütünleşik afet yönetim süre-cinde afet zararlarının azaltılması amacıyla geçmişe nazaran teknoloji desteği çok daha yaygın kullanılmaya başlamıştır. Dünyada ortaya çıkan büyük veriler kullanılarak yapılan analiz ve çalışmalar, yapay zekâ kullanılarak oluşturulan programlar, modellemeler ve eğitimler, blok zincir sistemi ile oluşturulan veri sistemleri, afet öncesi, sırası ve sonrasında kullanılarak zarar görebilirlik düzeyi azaltılmaya çalışılmaktadır. Bu çalışmada da bütünleşik afet yönetim süreçle-rinde veri kullanım sistemleri ile bu sistemlerin afet yönetimi ile ilişkisi detay-landırılacaktır. Giriş Geçmişten günümüze afetlerin sayısı ve şiddeti gittikçe artmaktadır (CRED, 2022). Her geçen gün yıkıcılığı artan afetlerin önlenmesi veya ortaya çıkarabileceği zararları en aza indirebilmek için ülkeler ve topluluklar daha çok çaba sarf etmektedir. Afet yö-netiminin her aşamasında başarı sağlayabilmek için her geçen gün gelişen ve değişen teknolojinin kullanılması artık vazgeçilmez bir unsurdur. Afet yönetiminde teknolojik
The 2016 Kumamoto earthquake sequence was triggered by an Mw 6.2
event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April,
an Mw 7.0 event (the mainshock) followed. The epicenters of both
events were located near the residential area of Mashiki and affected the
region nearby. Due to very strong seismic ground motion, the earthquake
produced extensive damage to buildings and infrastructure. In this paper,
collapsed buildings were detected using a pair of digital surface
models (DSMs), taken before and after the 16 April mainshock by airborne
light detection and ranging (lidar) flights. Different methods were evaluated
to identify collapsed buildings from the DSMs. The change in average
elevation within a building footprint was found to be the most important
factor. Finally, the distribution of collapsed buildings in the study area
was presented, and the result was consistent with that of a building damage
survey performed after the earthquake.
