Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake

Tong, Xiaohua; Hong, Zhonghua; Liu, Shijie; Zhang, Xue; Xie, Huan; Zhengyuan, Li; Yang, Sonlin; Wang, Weian; Bao, Feng

doi:10.1016/j.isprsjprs.2011.12.004

Cited by 135 publications

(58 citation statements)

References 53 publications

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“…Generally, we can categorize those methods into two types as: detecting the collapsed buildings (1) by measuring the difference between the preseismic and postseismic digital surface models (hereafter called as DSMs) created from the aerial images of target area, and (2) by checking changes (including height or shadow) in the ground 3D models created by the light detection and ranging (LIDAR) system. Tong et al [6,7] brought an accurate detection algorithm of the collapsed building by comparing the changes in preseismic and postseismic digital elevation model (hereafter called as DEMs) which are created by the high-resolution satellite IKONOS stereo images. In [6], they detected the collapsed buildings by comparing the height changes in the DEM before and after earthquake, where huge height changes correspond to a collapsed building.…”

Section: Related Workmentioning

confidence: 99%

“…Tong et al [6,7] brought an accurate detection algorithm of the collapsed building by comparing the changes in preseismic and postseismic digital elevation model (hereafter called as DEMs) which are created by the high-resolution satellite IKONOS stereo images. In [6], they detected the collapsed buildings by comparing the height changes in the DEM before and after earthquake, where huge height changes correspond to a collapsed building. While in [7], if the shadow of a building changes greatly before and after the earthquake, it indicates that such a building is a collapsed one.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, without the human prior knowledge, when applied to a full aerial image, the performance of this method is suspected. Since covering all the researches about detecting the collapsed buildings is beyond the scope of this paper, more detailed survey could be found in [6,7].…”

Section: Related Workmentioning

confidence: 99%

“…At each image pixel (x, y), whose color is represented as (R x,y , G x,y , B x,y ), a Gaussian kennel mask C(x, y) is used to describe the color similarity The detection and integration of feature points from collapsed buildings. (a) The detected feature points of collapsed buildings from (6). Here, since one feature point is too small to be shown clearly, each feature point is represented by one 5 × 5 rectangle; and (b) the produced candidate of collapsed buildings by the variable mean shift clustering [17].…”

Section: Appearance Analysismentioning

confidence: 99%

“…Another important parameter that should be investigated is the S(x, y), the top row of (6). Figure 12 shows that increasing the threshold of S(x, y) within a small range will not affect the final detection results.…”

Section: Effect Of Parametersmentioning

confidence: 99%

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Detection of collapsed buildings with the aerial images captured from UAV

Hua

Shang

et al. 2015

Sci. China Inf. Sci.

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In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle (UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high value. The candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise, each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Appearance Analysismentioning

confidence: 99%

Section: Effect Of Parametersmentioning

confidence: 99%

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Detection of collapsed buildings with the aerial images captured from UAV

Hua

Shang

et al. 2015

Sci. China Inf. Sci.

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A Review on Multi‐temporal Earthquake Damage Assessment Using Satellite Images

Taşkın

Erten

Alataş

2021

Change Detection and Image Time Series Analysis 2

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Mechano‐Responsive Spiropyran Microbeads: A Facile Fabrication Strategy for Self‐Reporting Materials

Jang

Park

et al. 2022

Adv Materials Technologies

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irreversible catastrophic disasters. Thus, to ensure the long-term stability of structures, damage diagnosis technology that can accurately detect the occurrence of cracks in real time is urgently required. [1] Two types of diagnostic methods are often employed: contact diagnostics, in which an electrical signal is detected by a n attached strain gauge [2] or piezoelectric sensor, [3] and noncontact diagnostics, in which a camera [4] or LiDAR detector [4b,5] is employed. Recently, research on selfreporting materials, which can produce visible signs of deformation or damage via the incorporation of force-responsive molecules called mechanophores, began to be conducted. [6] These materials are now attracting increasing levels of attention because of their ability to monitor nonuniform stress/strain distributions in real time, without the need for expensive power sources and data-processing devices.Spiropyran (SP) is a type of mechanophore that exhibits a specific reaction in response to force. [6e,7] As shown in Scheme 1, when a force is applied, the SP molecule undergoes selective C-O bond cleavage and is converted into merocyanine (MC), which is accompanied by color and fluorescence changes. SP is pale yellow and does not fluoresce, but MC is blue (or purple) and exhibits strong fluorescence in the 550-700 nm wavelength region. The advantages of stress visualization, high sensitivity, and reversible response have resulted in SP being very actively studied recently. Since 2009, when Moore and co-workers [7b] reported the discovery of the self-reporting of external forces by chemically bonding SP to a polymethacrylate polymer structure, SP has been introduced into various polymers such as polyurethane (PU), [8] polydimethylsiloxane (PDMS), [9] polyamide (PA), [10] polyester (PET), [11] polyacrylates, [6a,12] and polystyrene. [13] For example, Braun and co-workers [8a] successfully used SP as a molecular probe of force and orientation by incorporating it into the hard and soft phase of a segmented PU, respectively. Ko and co-workers [14] dramatically improved the mechano-sensitivity of SP-linked PDMS by adopting a hierarchical nanoparticle-inmicropore architecture and applied it as a mechano-chromic electronic skin. The nonuniform stress states of a SP-linked polyethylacrylate were experimentally monitored by Creton and co-workers, [15] and their observations were in accord with finite element simulation results. Spiropyran (SP) mechanophores are attracting attention as next-generationsmart materials that can self-diagnose stress or strain thanks to their capacity for stress visualization with superior sensitivity. However, at present, to achieve the self-reporting functionality, it is considered essential that SP is chemically bonded to a host matrix, which has greatly limited its application. In this paper, mechano-responsive SP beads that can render a material selfreporting by means of simple physical mixing are presented. The synthesis of SP beads is achieved in a microemulsifying needle via dispersio...

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Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake

Cited by 135 publications

References 53 publications

Detection of collapsed buildings with the aerial images captured from UAV

Detection of collapsed buildings with the aerial images captured from UAV

A Review on Multi‐temporal Earthquake Damage Assessment Using Satellite Images

Mechano‐Responsive Spiropyran Microbeads: A Facile Fabrication Strategy for Self‐Reporting Materials

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