Towards Rapid Citywide Damage Mapping Using Neighborhood Edge Dissimilarities in Very High-Resolution Optical Satellite Imagery—Application to the 2003 Bam, Iran, Earthquake

Huyck, Charles; Adams, Beverley J.; Cho, Sungbin; Chung, Hung-Chi; Eguchi, Ronald T.

doi:10.1193/1.2101907

Cited by 41 publications

(16 citation statements)

References 6 publications

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“…Previous researchers have used pre-and post-earthquake images to map earthquake-induced effects Kohiyama and Yamazaki, 2005;Mansouri et al, 2005;Rathje et al, 2005;Huyck et al, 2006;Kayen et al, 2006;Rathje et al, 2006;and Eguchi et al, 2010). These images are often used to aid reconnaissance teams (Hisada et al, 2005;Huyck et al, 2005). The current approach of computer-based processing of these images to support reconnaissance efforts falls into two general categories: (1) use of pre-and postearthquake data to identify change, and (2) use of only post-earthquake imagery to identify damage.…”

Section: Introductionmentioning

confidence: 99%

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

Oommen

Baise

Gens

et al. 2013

Environmental & Engineering Geoscience

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Documenting earthquake-induced liquefaction effects is important to validate empirical liquefaction susceptibility models and to enhance our understanding of the liquefaction process. Currently, after an earthquake, field-based mapping of liquefaction can be sporadic and limited due to inaccessibility and lack of resources. Alternatively, researchers have used change detection with remotely sensed pre-and postearthquake satellite images to map earthquake-induced effects. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, liquefaction-induced surface changes should be associated with increased moisture, and spectral bands/transformations that are sensitive to soil moisture can be used to identify these areas. We verify our hypothesis using change detection with pre-and postearthquake thermal and tasseled cap wetness images derived from available Landsat 7 Enhanced Thematic Mapper Plus (ETM + ) for the 2001 Bhuj earthquake in India. The tasseled cap wetness image is directly related to the soil moisture content, whereas the thermal image is inversely related to it. The change detection of the tasseled cap transform wetness image helped to delineate earthquake-induced liquefaction areas that corroborated well with previous studies. The extent of liquefaction varied within and between geomorphological units, which we believe can be attributed to differences in the soil moisture retention capacity within and between the geomorphological units.

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Section: Introductionmentioning

confidence: 99%

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

Oommen

Baise

Gens

et al. 2013

Environmental & Engineering Geoscience

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“…The former are based on the application of change detection algorithms using both pre-and post-event images. Several papers have already presented promising results achieved using this approach (Huyck et al, 2005;Yamazaki et al, 2008;Tomowski et al, 2010). The damage assessment usually consists of four main phases: image registration, object extraction, attributes (features) collection and classification.…”

Section: General Contextmentioning

confidence: 99%

Automated processing of high resolution airborne images for earthquake damage assessment

Nex

Rupnik

Toschi

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Emergency response ought to be rapid, reliable and efficient in terms of bringing the necessary help to sites where it is actually needed. Although the remote sensing techniques require minimum fieldwork and allow for continuous coverage, the established approaches rely on a vast manual work and visual assessment thus are time-consuming and imprecise. Automated processes with little possible interaction are in demand. This paper attempts to address the aforementioned issues by employing an unsupervised classification approach to identify building areas affected by an earthquake event. The classification task is formulated in the Markov Random Fields (MRF) framework and only post-event airborne high-resolution images serve as the input. The generated photogrammetric Digital Surface Model (DSM) and a true orthophoto provide height and spectral information to characterize the urban scene through a set of features. The classification proceeds in two phases, one for distinguishing the buildings out of an urban context (urban classification), and the other for identifying the damaged structures (building classification). The algorithms are evaluated on a dataset consisting of aerial images (7cm GSD) taken after the Emilia-Romagna (Italy) earthquake in 2012.

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“…This method was based on the assumption that after the earthquake in the collapsed buildings more dissimilarities of edge information are observed in comparison with the intact buildings (Huyck, Cho, Chung, & Eguchi, 2005). Li et al, proposed a method based on a combination of spectral and location information in the detection of collapsed buildings using QuickBird satellite data after the Wenchuan earthquake, China.…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

zahraee

Rastiveis

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre-and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre-and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

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Towards Rapid Citywide Damage Mapping Using Neighborhood Edge Dissimilarities in Very High-Resolution Optical Satellite Imagery—Application to the 2003 Bam, Iran, Earthquake

Cited by 41 publications

References 6 publications

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

Documenting Earthquake-Induced Liquefaction Using Satellite Remote Sensing Image Transformations

Automated processing of high resolution airborne images for earthquake damage assessment

Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

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