scholarly journals PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH OBJECT-BASED IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 347-350 ◽  
Author(s):  
A. Sabuncu ◽  
Z. D. Uca Avci ◽  
F. Sunar
Keyword(s):  
Damage Detection ◽  
Image Classification ◽  
Building Damage ◽  
Damage Rate ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Damage Grade ◽  
Land Surfaces ◽  
Collapsed Buildings

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. <br><br> In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (© Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

scholarly journals PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH OBJECT-BASED IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 347-350
Author(s):  
A. Sabuncu ◽  
Z. D. Uca Avci ◽  
F. Sunar
Keyword(s):  
Damage Detection ◽  
Image Classification ◽  
Building Damage ◽  
Damage Rate ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Damage Grade ◽  
Land Surfaces ◽  
Collapsed Buildings

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. <br><br> In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (© Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

scholarly journals Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1146
Author(s):  
Yuliang Nie ◽  
Qiming Zeng ◽  
Haizhen Zhang ◽  
Qing Wang
Keyword(s):  
Damage Detection ◽  
Texture Features ◽  
Classification Performance ◽  
Building Damage ◽  
Alos Palsar ◽  
Yushu Earthquake ◽  
Matching Algorithm ◽  
2011 Tohoku Tsunami ◽  
Collapsed Buildings ◽  
Study Sites

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

scholarly journals OBJECT-ORIENTED CLASSIFICATION OF LIDAR DATA FOR POST-EARTHQUAKE DAMAGE DETECTION

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 421-427
Author(s):  
H. Rastiveis ◽  
N. Khodaverdi zahraee ◽  
A. Jouybari
Keyword(s):  
Damage Detection ◽  
Satellite Image ◽  
Building Damage ◽  
Support Vector ◽  
Lidar Data ◽  
Data Set ◽  
Image Objects ◽  
Object Based ◽  
Training Samples ◽  
Earthquake Damage Detection

<p><strong>Abstract.</strong> The collapse of buildings during the earthquake is a major cause of human casualties. Furthermore, the threat of earthquakes will increase with growing urbanization and millions of people will be vulnerable to earthquakes. Therefore, building damage detection has gained increasing attention from the scientific community. The advent of Light Detection And Ranging (LiDAR) technique makes it possible to detect and assess building damage in the aftermath of earthquake disasters using this data. The purpose of this paper is to propose and implement an object-based approach for mapping destructed buildings after an earthquake using LiDAR data. For this purpose, first, multi-resolution segmentation of post-event LiDAR data is done after building extraction from pre-event building vector map. Then obtained image objects from post-event LiDAR data is located on the pre-event satellite image. After that, appropriate features, which make a better difference between damage and undamaged buildings, are calculated for all the image objects on both data. Finally, appropriate training samples are selected and imported into the object-based support vector machine (SVM) classification technique for detecting the building damage areas. The proposed method was tested on the data set after the 2010 earthquake of Port-au-Prince, Haiti. Quantitative evaluation of results shows the overall accuracy of 92&amp;thinsp;% by this method.</p>

Object-based post-fire aerial image classification for building damage, destruction and defensive actions at the 2012 Colorado Waldo Canyon Fire

2020 ◽  
Vol 29 (2) ◽  
pp. 174
Author(s):  
Derek McNamara ◽  
William Mell ◽  
Alexander Maranghides
Keyword(s):  
Image Classification ◽  
Building Damage ◽  
Aerial Imagery ◽  
Aerial Image ◽  
Water Suppression ◽  
Kappa Statistics ◽  
Substantial Agreement ◽  
Full Extent ◽  
Active Fire ◽  
Object Based

We compare the use of post-fire aerial imagery to ground-based assessment for identifying building destruction and damage at the 2012 Colorado Waldo Canyon Fire. We also compare active-fire defensive actions identified via manual and automated post-fire image classification to defensive actions documented from ground-based assessments (witness discussions, vehicle logs and images). For building destruction, manual and automatic image classification compared favourably to ground-based assessment, with low errors of commission (0.0–0.4%) and omission (0–1.2%). For building damage, classifying imagery manually had significant errors of commission and omission (59.0% and 57.9%) because ground-based assessments missed roof damage and image classification excluded interior and side damage, indicating the need for both techniques. Classifying imagery automatically for indicators of active-fire water suppression on buildings has Kappa statistics indicating a substantial agreement with documented water suppression. Manual and automatic image classification underestimated the full extent of documented defensive actions but showed a statistically significant dependence between fire cessation and defensive actions. These results show post-fire imagery to be a useful addition to other techniques for identifying building damage, destruction and defensive actions. Also demonstrated is the importance of accounting for defensive actions and other factors in wildland–urban interface fire studies.

Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake

2004 ◽  
Vol 20 (1) ◽  
pp. 145-169 ◽  
Author(s):  
Keiko Saito ◽  
Robin J. S. Spence ◽  
Christopher Going ◽  
Michael Markus
Keyword(s):  
Damage Assessment ◽  
Satellite Images ◽  
Satellite Image ◽  
Building Damage ◽  
Large Area ◽  
Rapid Visual Screening ◽  
High Resolution Satellite Images ◽  
Collapsed Buildings ◽  
Optical Satellite Images ◽  
The City

Newly available optical satellite images with 1-m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post-event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post-earthquake cover of the city of Bhuj, which was close to the epicenter, and pre- and post-earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building-by-building damage survey. In the third experiment, pre- and post-earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.

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