The Experts below are selected from a list of 47763 Experts worldwide ranked by ideXlab platform
Paolo Gamba - One of the best experts on this subject based on the ideXlab platform.
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remote sensing and Earthquake Damage assessment experiences limits and perspectives
Proceedings of the IEEE, 2012Co-Authors: Fabio Dellacqua, Paolo GambaAbstract:In this paper, a survey of the techniques and data sets used to evaluate Earthquake Damages using remote sensing data is presented. After a few preliminary definitions about Earthquake Damage, their evaluation scale, and the difference between identification of Damage “extent” and identification of Damage “level,” the advantages and limits of different remote sensing data sets are presented. Furthermore, a survey of proposed algorithms for data interpretation and Earthquake Damage extraction is presented, and two examples of these algorithms and their results are discussed. According to the outcome of this survey, some open issues are finally presented and discussed, identifying possible research lines as well as working solutions.
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mapping Earthquake Damage in vhr radar images of human settlements preliminary results on the 6 th april 2009 italy case
International Geoscience and Remote Sensing Symposium, 2010Co-Authors: Fabio Dellacqua, Paolo Gamba, Diego PolliAbstract:Automated Earthquake Damage assessment from post-event only remotely sensed data is highly desirable, especially when new generation, Very High Resolution (VHR) spaceborne data is concerned, lacking extensive pre-event archives. Though, most Damage assessment method either rely on human interpretation or on pre-post-event comparison. In this paper we illustrate some possible tracks for investigating Damage assessment on post-event only data, focusing on the 6th April 2009 Abruzzi, Italy Earthquake and on related COSMO/SkyMed acquisitions.
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rapid Damage detection in the bam area using multitemporal sar and exploiting ancillary data
IEEE Transactions on Geoscience and Remote Sensing, 2007Co-Authors: Paolo Gamba, Fabio Dellacqua, G TrianniAbstract:In this paper, the problem of rapid Earthquake Damage detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the Damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid "class-blurring" effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Bam, Iran, and compared with ground-based survey maps
Fabio Dellacqua - One of the best experts on this subject based on the ideXlab platform.
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remote sensing and Earthquake Damage assessment experiences limits and perspectives
Proceedings of the IEEE, 2012Co-Authors: Fabio Dellacqua, Paolo GambaAbstract:In this paper, a survey of the techniques and data sets used to evaluate Earthquake Damages using remote sensing data is presented. After a few preliminary definitions about Earthquake Damage, their evaluation scale, and the difference between identification of Damage “extent” and identification of Damage “level,” the advantages and limits of different remote sensing data sets are presented. Furthermore, a survey of proposed algorithms for data interpretation and Earthquake Damage extraction is presented, and two examples of these algorithms and their results are discussed. According to the outcome of this survey, some open issues are finally presented and discussed, identifying possible research lines as well as working solutions.
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mapping Earthquake Damage in vhr radar images of human settlements preliminary results on the 6 th april 2009 italy case
International Geoscience and Remote Sensing Symposium, 2010Co-Authors: Fabio Dellacqua, Paolo Gamba, Diego PolliAbstract:Automated Earthquake Damage assessment from post-event only remotely sensed data is highly desirable, especially when new generation, Very High Resolution (VHR) spaceborne data is concerned, lacking extensive pre-event archives. Though, most Damage assessment method either rely on human interpretation or on pre-post-event comparison. In this paper we illustrate some possible tracks for investigating Damage assessment on post-event only data, focusing on the 6th April 2009 Abruzzi, Italy Earthquake and on related COSMO/SkyMed acquisitions.
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rapid Damage detection in the bam area using multitemporal sar and exploiting ancillary data
IEEE Transactions on Geoscience and Remote Sensing, 2007Co-Authors: Paolo Gamba, Fabio Dellacqua, G TrianniAbstract:In this paper, the problem of rapid Earthquake Damage detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the Damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid "class-blurring" effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Bam, Iran, and compared with ground-based survey maps
Naichi Hsiao - One of the best experts on this subject based on the ideXlab platform.
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relationship between peak ground acceleration peak ground velocity and intensity in taiwan
Bulletin of the Seismological Society of America, 2003Co-Authors: Yih-min Wu, Taliang Teng, Tzay-chyn Shin, Naichi HsiaoAbstract:Based on the strong-motion data set from the 1999 Chi-Chi, Taiwan, Earthquake and a shaking Damage statistics database, we investigated the correlations between strong ground motions and Earthquake Damage (fatalities and building collapses) through a regression analysis. As a result, the current Earthquake intensity scale I t is placed on a more reliable instrumental basis. This is necessary for the real-time seismic monitoring operation in Taiwan where programs for Earthquake rapid reporting (RRS) and Earthquake early warning (EWS) are actively pursued. It is found that the Earthquake Damage statistics give a much closer correlation with the peak ground velocity (PGV) than with the peak ground acceleration (PGA). The empirical relationship between PGV and the intensity I t determined in this study can be expressed as \[I_{\mathrm{t}}=2.14{\times}\mathrm{log}_{10}(\mathrm{PGV})+1.89.\] This PGV-based intensity is particularly useful in real-time applications for Damage prediction and assessment, as the Damage impact of high PGV is much more important for mid-rise and high-rise buildings that are characteristic of a modern society. For smaller Earthquakes ( M M
James B Campbell - One of the best experts on this subject based on the ideXlab platform.
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detection of urban Damage using remote sensing and machine learning algorithms revisiting the 2010 haiti Earthquake
Remote Sensing, 2016Co-Authors: Austin J Cooner, Yang Shao, James B CampbellAbstract:Remote sensing continues to be an invaluable tool in Earthquake Damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting Earthquake Damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting Damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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Earthquake Damage assessment of buildings using vhr optical and sar imagery
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: D Brunner, Guido Lemoine, Lorenzo BruzzoneAbstract:Rapid Damage assessment after natural disasters (e.g., Earthquakes) and violent conflicts (e.g., war-related destruction) is crucial for initiating effective emergency response actions. Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner. In this paper, we present a novel method that detects buildings destroyed in an Earthquake using pre-event VHR optical and post-event detected VHR SAR imagery. The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated. First, the 3-D parameters of a building are estimated from the pre-event optical imagery. Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event. Third, the similarity between the predicted image and the actual SAR image is analyzed. If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed. A similarity threshold is used to classify the buildings. We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily Damaged in the Sichuan Earthquake of May 12, 2008. For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data.
