Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data

Gamba, Paolo; Dell’Acqua, Fabio; Trianni, G.

doi:10.1109/tgrs.2006.885392

Cited by 133 publications

(71 citation statements)

References 17 publications

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“…In recent years, several systems have been developed estimating the extent and type of destruction on various buildings. The damage assessment was realized using different kinds of sensors such as light detection and ranging (LIDAR; Rehor et al 2008), SAR data (Gamba et al 2007) and high-resolution spaceborne (Chesnel et al 2007) and airborne images (Guo et al 2009). However, most methods focus on only one single sensor and, thus, the adaptability is limited depending on the availability of data sources after a natural hazard.…”

Section: Infrastructure Assessment Systemsmentioning

confidence: 99%

Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery

Butenuth

Frey

Nielsen

et al. 2011

International Journal of Remote Sensing

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(2011): Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery, International Journal of Remote Sensing, 32:23,[8575][8576][8577][8578][8579][8580][8581][8582][8583][8584][8585][8586][8587][8588][8589][8590][8591][8592][8593][8594] To link to this article: http://dx.doi.org/10.1080/01431161.2010.542204 PLEASE SCROLL DOWN FOR ARTICLEFull terms and conditions of use: http://www.tandfonline.com/page/terms-andconditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Vol. 32, No. 23, 10 December 2011, 8575-8594 Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery In this article, a new assessment system is presented to evaluate infrastructure objects such as roads after natural disasters in near-realtime. A particular aim is the exploitation of multi-sensor and multi-temporal imagery together with further geographic information system data in a comprehensive assessment framework. The combination is accomplished combining probabilities derived from the different data sets. The assessment system is applied to two different test scenarios evaluating roads after flooding, yielding very promising results and evaluation values concerning completeness and correctness. The benefit of the data combination, in particular the multi-temporal component, demonstrates the suitability of the proposed method for different application scenarios. International Journal of Remote Sensing

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Section: Infrastructure Assessment Systemsmentioning

confidence: 99%

Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery

Butenuth

Frey

Nielsen

et al. 2011

International Journal of Remote Sensing

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“…The authors propose technical solutions for automatic change detection, using the Indian Ocean tsunami 2006 disaster as a case study. Another example of the application of remote sensing data for rapid damage detection is described in [7] for the 2005 Bam earthquake in Iran. Many technical papers on EO data processing methodologies increasingly address themes that are relevant to emergency response e.g., [8]- [11] and [12].…”

Section: A Backgroundmentioning

confidence: 99%

Distributed Geospatial Data Processing Functionality to Support Collaborative and Rapid Emergency Response

Brunner

Lemoine

Thoorens

et al. 2009

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-This paper presents a novel approach to integrate the latest generation very high-resolution earth observation imagery into the operational workflow of geospatial information support for emergency response actions. The core concept behind this approach is the implementation of an image pyramid structure that allows each image tile to be addressed separately. We propose a novel way to collate geospatial feature data from distributed sources and integrate them in visualization and image processing. The system components enable rapid collaborative mapping, support for in situ data collection, customized on-demand image processing, and geospatial data queries and near instantaneous map visualization. We adapt functional software modules that are available in the public and open source domain. The approach is demonstrated with a test case in a rapid damage assessment scenario using very high-resolution optical satellite QuickBird and IKONOS imagery over Southern Lebanon from 2006.Index Terms-Collaborative mapping, feature extraction, geospatial queries, image processing, information management, on-demand processing, visualization, web services.

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“…After an earthquake, rapid damage assessment is vital for emergency response actions, rescue work and post-disaster reconstruction. In recent decades, various kinds of remote sensing data, such as aerial or satellite images, Lidar and SAR, have been widely used to identify, detect and assess for natural disasters [1] [2]. Therefore, the accuracy classification of individual building damaged types using the remote sensing images has become a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d Gis Model

Tu¹,

Sui²,

Feng³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract:In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre-and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

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Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data

Cited by 133 publications

References 17 publications

Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery

Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery

Distributed Geospatial Data Processing Functionality to Support Collaborative and Rapid Emergency Response

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d Gis Model

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