Use of Remote Sensing Technologies for Building Damage Assessment after the 2003 Bam, Iran, Earthquake—Preface to Remote Sensing Papers

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 207-212 ◽  
Author(s):  
Ronald T. Eguchi ◽  
Babak Mansouri

This preface introduces a series of papers that describe the use of remote sensing technologies in quantifying the extent of building damage after the 2003 Bam, Iran, earthquake. These papers represent a significant milestone in post-earthquake loss estimation. For the first time, independent evaluations of regional damage are documented, which will ultimately allow an assessment of the efficacy of these technologies as tools for post-earthquake damage detection and quantification. Not only were different sensors used, but radically different approaches were implemented in quantifying damage. The conclusions and recommendations of the different papers are generally consistent and strongly suggest that regional damage assessment using remotely sensed data is highly feasible. The papers, however, acknowledge that more research is needed before these technologies can be used to make critical emergency response decisions. Finally, the role of the Earthquake Engineering Research Institute through its Learning From Earthquakes Program is acknowledged, largely for helping to promote the use of remote sensing technologies in earthquake studies and for recognizing the value of collaboration through its newly formed Subcommittee on Remote Sensing.

1997 ◽  
Vol 13 (4) ◽  
pp. 739-758 ◽  
Author(s):  
Masanobu Shinozuka ◽  
Stephanie E. Chang ◽  
Ronald T. Eguchi ◽  
Daniel P. Abrams ◽  
Howard H. M. Hwang ◽  
...  

In recent years, a number of research efforts conducted through the National Center for Earthquake Engineering Research (NCEER) have focused on assessing seismic hazard and vulnerability in the Central United States. These multi-year, coordinated multi-investigator research efforts culminated in two loss estimation demonstration projects for Memphis (Shelby County), Tennessee, that evaluate losses associated with buildings and lifelines, respectively. While conducted independently, these two loss estimation studies share similar approaches, such as the emphasis on using detailed local data. Furthermore, the significance of the projects derives not only from the advances made by individual investigators, but also from the innovations developed in synthesizing the various studies into a coordinated loss estimation effort. This paper discusses the NCEER buildings and lifelines loss estimation projects with emphasis on methodological advances and insights from the loss estimation results.


Author(s):  
Asset Akhmadiya ◽  
Nabi Nabiyev ◽  
Khuralay Moldamurat ◽  
Kanagat Dyusekeev ◽  
Sabyrzhan Atanov

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.


2022 ◽  
pp. 509-521
Author(s):  
Mohammad Kakooei ◽  
Arsalan Ghorbanian ◽  
Yasser Baleghi ◽  
Meisam Amani ◽  
Andrea Nascetti

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 213-218 ◽  
Author(s):  
Beverley J. Adams ◽  
Babak Mansouri ◽  
Charles K. Huyck

Advanced technologies, such as remote sensing, have considerable potential for increasing the effectiveness of post-disaster reconnaissance. In the aftermath of the Bam earthquake, the EERI field team deployed the VIEWS™ (Visualizing Impacts of Earthquakes With Satellites) reconnaissance system to support urban damage assessment activities. This paper introduces the VIEWS™ system and describes its inaugural implementation for earthquake response. For the Bam deployment, VIEWS™ integrated city-wide base layers of 60 cm color QuickBird satellite imagery collected “before” and “after” the event, with a real-time GPS (Global Positioning System) feed. The satellite imagery helped direct team members to the hardest hit areas, and real-time tracking supported efficient route planning, progress monitoring, and the capture of geo-referenced digital photographs. Through the VIEWS™ visualization mode, researchers are able to replay and analyze the datasets that were collected. The VIEWS™ system was developed by ImageCat, Inc. in collaboration with the Multidisciplinary Center for Earthquake Engineering Research (MCEER).


2018 ◽  
Vol 51 (1) ◽  
pp. 991-1005 ◽  
Author(s):  
Silvana Cotrufo ◽  
Constantin Sandu ◽  
Fabio Giulio Tonolo ◽  
Piero Boccardo

2001 ◽  
Vol 17 (4) ◽  
pp. 635-656 ◽  
Author(s):  
Chin-Hsiung Loh ◽  
Ching-Yen Tsay

In the early morning of 21 September 1999, a devastating earthquake struck the central region of Taiwan. This earthquake became known as the “Chi-Chi” Taiwan earthquake. Immediately after the occurrence of the earthquake, the National Center for Research on Earthquake Engineering (NCREE) organized reconnaissance teams to investigate the damage in the earthquake-affected area. The purpose of this paper is to describe the inter-collaborations and actions that were taken by NCREE and the engineering research community. This paper also describes the damage situation from an engineering point of view that includes fault investigations, studies of strong ground motion characteristics, building and bridge damage investigations, geotechnical damage surveys, and lifeline damage investigations. The NCREE's emergency response decision support system and the HAZ-Taiwan earthquake loss assessment program are also described.


Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.


2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.


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