Classification of Collapsed Buildings for Fast Damage and Loss Assessment

2006 ◽  
Vol 4 (2) ◽  
pp. 177-192 ◽  
Author(s):  
Christine Schweier ◽  
Michael Markus
Keyword(s):  
Loss Assessment ◽  
Collapsed Buildings

Dynamic Oxygen-Enhanced MRI Versus Quantitative CT: Pulmonary Functional Loss Assessment and Clinical Stage Classification of Smoking-Related COPD

2008 ◽  
Vol 190 (2) ◽  
pp. W93-W99 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Hisanobu Koyama ◽  
Munenobu Nogami ◽  
Daisuke Takenaka ◽  
Sumiaki Matsumoto ◽  
...  
Keyword(s):  
Clinical Stage ◽  
Loss Assessment ◽  
Quantitative Ct ◽  
Functional Loss ◽  
Enhanced Mri ◽  
Stage Classification

Oxygen-enhanced MRI vs. quantitatively assessed thin-section CT: Pulmonary functional loss assessment and clinical stage classification of asthmatics

2011 ◽  
Vol 77 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Hisanobu Koyama ◽  
Keiko Matsumoto ◽  
Yumiko Onishi ◽  
Munenobu Nogami ◽  
...  
Keyword(s):  
Thin Section ◽  
Clinical Stage ◽  
Loss Assessment ◽  
Functional Loss ◽  
Enhanced Mri ◽  
Stage Classification

scholarly journals DAMAGE DETECTION ON BUILDING FAÇADES USING MULTI-TEMPORAL AERIAL OBLIQUE IMAGERY

2019 ◽  
Vol IV-2/W5 ◽  
pp. 29-36 ◽  
Author(s):  
D. Duarte ◽  
F. Nex ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Remote Sensing ◽  
Damage Detection ◽  
Binary Classification ◽  
Damage Analysis ◽  
Remote Sensing Imagery ◽  
Building Facades ◽  
Multi Temporal ◽  
Optimal Fusion ◽  
Collapsed Buildings

<p><strong>Abstract.</strong> Over the past decades, a special interest has been given to remote-sensing imagery to automate the detection of damaged buildings. Given the large areas it may cover and the possibility of automation of the damage detection process, when comparing with lengthy and costly ground observations. Currently, most image-based damage detection approaches rely on Convolutional Neural Networks (CNN). These are used to determine if a given image patch shows damage or not in a binary classification approach. However, such approaches are often trained using image samples containing only debris and rubble piles. Since such approaches often aim at detecting partial or totally collapsed buildings from remote-sensing imagery. Hence, such approaches might not be applicable when the aim is to detect façade damages. This is due to the fact that façade damages also include spalling, cracks and other small signs of damage. Only a few studies focus their damage analysis on the façade and a multi-temporal approach is still missing. In this paper, a multi-temporal approach specifically designed for the image classification of façade damages is presented. To this end, three multi-temporal approaches are compared with two mono-temporal approaches. Regarding the multi-temporal approaches the objective is to understand the optimal fusion between the two imagery epochs within a CNN. The results show that the multi-temporal approaches outperform the mono-temporal ones by up to 22% in accuracy.</p>

scholarly journals Calibration Of Regional Vulnerability Functions By Applying Earthquake Events Database

2020 ◽  
Vol 13 (4) ◽  
pp. 54-64
Author(s):  
Nina I. Frolova ◽  
Valery I. Larionov ◽  
Jean Bonnin ◽  
Sergey P. Sushchev ◽  
Alexander N. Ugarov
Keyword(s):  
Emergency Situation ◽  
Large Earthquake ◽  
Test Site ◽  
Calibration Procedure ◽  
Loss Assessment ◽  
Strong Earthquakes ◽  
Regional Vulnerability ◽  
Earthquake Vulnerability ◽  
Collapsed Buildings ◽  
Vulnerability Functions

The paper describes the structure and content of the Information System database containing information on earthquake events, which is developed and supported within the framework of computer support for the EMERCOM of the Russian Federation. The database is assigned to provide analytical support for decision making in case of an emergency situation, including tools for mathematical simulation of hazardous excitation, the response of elements at risk to excitation and loss generation. The calibration procedure of the earthquake vulnerability functions for buildings and structures using the database with descriptions of events is presented. The calibrated functions of earthquake vulnerability for buildings of different types are applied to provide an acceptable accuracy of situational assessments for the case of a strong earthquake. The examples of earthquake damage estimations for the test site in Siberia showed that region-specific parameters in the vulnerability functions yield more reliable results to estimate possible damage and losses due to a large earthquake. For Irkutsk City, the estimates of the numbers of heavily damaged and completely collapsed buildings obtained when using different sets of parameters for vulnerability functions differ by 30%. Such difference in damage estimates can significantly affect the plans for rescue and recovery operations. The conclusion is made about the advantage of the calibrated functions application for near real-time damage and loss assessment due to strong earthquakes in order to ensure population safety and territory sustainable development.

scholarly journals Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

2018 ◽  
Vol 10 (2) ◽  
pp. 296 ◽  
Author(s):  
Luis Moya ◽  
Luis Marval Perez ◽  
Erick Mas ◽  
Bruno Adriano ◽  
Shunichi Koshimura ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Unsupervised Classification ◽  
Fragility Functions ◽  
Collapsed Buildings

scholarly journals Classification of Building Damage Triggered by Earthquakes Using Decision Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shaodan Li ◽  
Hong Tang
Keyword(s):  
Decision Tree ◽  
Satellite Images ◽  
Field Survey ◽  
Building Damage ◽  
Multiple Source ◽  
Decision Tree Method ◽  
Collapsed Buildings ◽  
Uav Images ◽  
Tree Method

Field survey is a labour-intensive way to objectively evaluate the grade of building damage triggered by earthquakes. In this paper, we present a decision-tree-based approach to classify the type of building damage by using multiple-source remote sensing from both pre- and postearthquakes. Specifically, the boundary of buildings is delineated from preearthquake multiple-source satellite images using an unsupervised learning method. Then, building damage is classified into four types using decision tree method from postearthquake UAV images, that is, basically intact buildings, slightly damaged buildings, partially collapsed buildings, and completely collapsed buildings. Furthermore, the slightly damaged buildings are determined by the detected roof-holes using joint color and height features. Two experimental areas from Wenchuan and Ya’an earthquakes are used to verify the proposed method.

scholarly journals Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

2019 ◽  
Vol 11 (8) ◽  
pp. 897 ◽  
Author(s):  
Wei Zhai ◽  
Chunlin Huang ◽  
Wansheng Pei
Keyword(s):  
Contrast Ratio ◽  
Texture Features ◽  
Building Damage ◽  
Building Collapse ◽  
Radar Imagery ◽  
Emergency Decision Making ◽  
Novel Method ◽  
Collapsed Buildings ◽  
Emergency Decision

After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

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