Detection of Building Damage Areas of the 2006 Central Java, Indonesia, Earthquake through Digital Analysis of Optical Satellite Images

2013 ◽  
Vol 29 (2) ◽  
pp. 453-473 ◽  
Author(s):  
Hiroyuki Miura ◽  
Saburoh Midorikawa ◽  
Norman Kerle
Keyword(s):  
Image Analysis ◽  
Satellite Images ◽  
Building Damage ◽  
Bare Ground ◽  
Central Java ◽  
Gis Data ◽  
Before And After ◽  
Collapsed Buildings ◽  
Optical Satellite Images ◽  
The Relationship

In order to evaluate the capability of building damage detection from optical satellite images, a procedure for digital image analysis is examined and applied to images captured before and after the 2006 Central Java, Indonesia, earthquake. In the image analysis, the pixels of the images are classified into vegetation, bare ground, and built-up areas. The damage areas are detected by the differential of the digital numbers in the built-up areas. The estimated damage distribution is validated by comparing it with the GIS data on building damage obtained from a field survey. The results show that the severely damaged areas were well detected by the analysis. In the densely vegetated areas, however, the damage was underestimated because many of the buildings were obscured by trees. For assessing quantitative damage information, the relationship between the number of collapsed buildings and the areas detected by the image analysis is evaluated.

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.

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

2020 ◽  
Vol 36 (1) ◽  
pp. 209-231
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Buildings ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision makers and for producing an effective emergency response. In this vein, this article presents the following: (1) statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) an investigation of the factors influencing the extent of debris; (3) probability functions for the debris extent; and (4) applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (16 April 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

Statistical Analysis of Japan Wood Frame Building Earthquake Debris Extent and Its Use in Road Networks in Japan

2019 ◽  
pp. 875529301987818
Author(s):  
Luis Moya ◽  
Erick Mas ◽  
Fumio Yamazaki ◽  
Wen Liu ◽  
Shunichi Koshimura
Keyword(s):  
Urban Areas ◽  
Road Networks ◽  
Decision Makers ◽  
Aerial Photos ◽  
Geometrical Properties ◽  
Frame Building ◽  
Before And After ◽  
Wood Frame ◽  
Collapsed Buildings ◽  
The Relationship

Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision-makers and for producing an effective emergency response. In this vein, this paper presents the following: (1) Statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) An investigation of the factors influencing the extent of debris; (3) Probability functions for debris extent; and (4) Applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (April 16, 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

Estimation of Tsunami-Inundated Areas in Asahi City, Chiba Prefecture, after the 2011 Tohoku-Oki Earthquake

2013 ◽  
Vol 29 (1_suppl) ◽  
pp. 201-217 ◽  
Author(s):  
Yoshihisa Maruyama ◽  
Ken Kitamura ◽  
Fumio Yamazaki
Keyword(s):  
Satellite Images ◽  
Pacific Coast ◽  
Field Survey ◽  
Source Region ◽  
Tsunami Propagation ◽  
Chiba Prefecture ◽  
The Pacific ◽  
Significant Damage ◽  
Collapsed Buildings ◽  
The Relationship

The 2011 off the Pacific coast of Tohoku-oki earthquake triggered an extremely large tsunami. The authors conducted a field survey in Asahi City, Chiba Prefecture, after the occurrence of the earthquake. Although located farther away from the source region of the earthquake, there was still significant damage in this area. Tsunami-inundated areas in Asahi City were identified from the map developed by disaster relief volunteers and the satellite images captured after the event. Polygons to demonstrate the tsunami-inundated areas were developed in the geographic information system. The authors compared the identified affected areas with the existing tsunami hazard map of Asahi City. The relationship between the tsunami-inundated areas and the locations of seawalls and tide-prevention forests was evaluated. In addition, a numerical simulation of tsunami propagation was performed and the ratio of totally collapsed buildings to the total number of buildings, that is, damage ratio, in terms of the estimated inundation depths was evaluated.

Extraction of Rural Building Damage due to Earthquake using Remote Sensing Imagery

2020 ◽  
Author(s):  
Shaodan Li ◽  
Hong Tang
Keyword(s):  
Remote Sensing ◽  
Visual Attention ◽  
Satellite Images ◽  
Spatial Information ◽  
Building Damage ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Chinese Restaurant ◽  
Collapsed Buildings ◽  
Uav Images

<p>In all kinds of natural disasters, earthquake is regarded as one of the greatest natural disaster in the world, and it seriously threats human's lives and properties. In the actual scene of earthquake disasters, the types of pre-earthquake satellite images available in the affected area are various, and they are from different sensors. However, the current researches on multi-source satellite image building recognition are not sufficient. In addition, when extracting building damage information, we can only determine whether the building is collapsed using the post-earthquake satellite images. Even the images have the sub-meter resolution, the identification of lightly damaged buildings is still a challenge. In order to solve the above problems, in this paper, we will use the post-earthquake UAV images and the pre-earthquake satellite images to extract the building damage information in rural areas of Sichuan, China. In particular, the main research contents of this paper are as follows:</p><ul><li>(1) According to the color feature of UAV images and the shape feature from point cloud data, we divide the building damage into four types: intact buildings, slightly damaged buildings, partially collapsed buildings and completely collapsed buildings, and give the rules of damage grades. In particular, the Chinese restaurant franchise model, which simultaneously fuses the color and shape features, is proposed to detect the earthquake-triggered roof-holes. Based on the roof-holes, the type of slightly damaged buildings is identificated.</li> <li>(2) At present, the model of building extraction from remote sensing images is suitable for an image, that is, for different images, the model needs to learn its model parameters again. In this paper, based on the generalized Chinese restaurant franchise (gCRF) model, we introduce the morphological profiles to propose the gCRF_MBI model. In the residential regions, the buildings are extracted by fusing the spatial information and the morphological profiles in the gCRF_MBI model.</li> <li>(3) The visual attention model selects the regions of interest from the complex scenes by simulating the visual attention mechanism of biological objects, which is similar to the extraction of residential regions from remote sensing images. In this paper, based on the basic principle of the spectral residual approach, we utilize the approach to extract the latent residential regions from remote sensing images, and we analyze the effects of different band combinations and different threshold methods on the extraction of residential regions.</li> </ul>

REMOTE SENSING TECHNOLOGIES IN POST-DISASTER DAMAGE ASSESSMENT

2007 ◽  
Vol 01 (03) ◽  
pp. 193-210 ◽  
Author(s):  
FUMIO YAMAZAKI ◽  
MASASHI MATSUOKA
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Damage Assessment ◽  
Satellite Images ◽  
Indian Ocean Tsunami ◽  
Event Correlation ◽  
2004 Indian Ocean Tsunami ◽  
Central Java ◽  
Before And After ◽  
Post Disaster

This paper highlights the recent applications of remote sensing technologies in post-disaster damage assessment, especially in the 2004 Indian Ocean tsunami and the 2006 Central Java earthquake. After the 2004 Indian Ocean tsunami, satellite images which captured the affected areas before and after the event were fully employed in field investigations and in tsunami damage mapping. Since the affected areas are vast, moderate resolution satellite images were quite effective in change detection due to the tsunami. Using high-resolution optical satellite images acquired before and after the 2006 Central Java earthquake, the areas of building damage were extracted based on pixel-based and object-based land cover classifications and their accuracy was compared with visual inspection results. In the Central Java earthquake, ALOS/PALSAR captured a SAR image of the affected area one day after the event as well as pre-event times. Taking the difference of the pre-event correlation and the pre-and-post event correlation, the areas affected by the earthquake were also identified. From these examples, the use of proper satellite imagery is suggested considering the area to cover, sensor type, spatial resolution, satellite's retake time etc., in post-disaster damage assessment.

scholarly journals HIGH-RESOLUTION OPTICAL SATELLITE IMAGES COLOR CONSISTENCY METHOD BASED ON EXTERNAL COLOR REFERENCES

2020 ◽  
Vol V-3-2020 ◽  
pp. 663-668
Author(s):  
H. Cui ◽  
G. Zhang
Keyword(s):  
Satellite Images ◽  
Atmospheric Correction ◽  
Low Frequency ◽  
Color Difference ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Dark Channel ◽  
Optical Satellite Images ◽  
The Relationship ◽  
Consistency Method

Abstract. Affected by factors such as season, illumination, atmospheric and sensor distortion, different satellite images often show obvious color difference, resulting in “stitching seams” at the edge of adjacent images, which seriously affects the application of satellite images. This study proposes a novel color consistency method for optical satellite images utilizing external color reference. Firstly, we improved the dark channel defogging method combining with the atmospheric distribution characteristics of satellite images, and used it to perform atmospheric correction on satellite images; Secondly, we corrected the color of atmospheric corrected satellite images through low-frequency signal replacement. Finally, we use a linear model to establish the relationship between high and low frequency signals, and stretching the high-frequency signal of images through local modelling. We selected two sets of representative experimental data for experiments, both the visual and quantitative obtained excellent results.

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