scholarly journals Transect Survey as a Post-Disaster Global Rapid Damage Assessment Tool

2015 ◽  
Vol 31 (4) ◽  
pp. 2443-2457 ◽  
Author(s):  
Lisa Moon ◽  
David Biggs ◽  
Jason Ingham ◽  
Michael Griffith
Keyword(s):  
Emergency Response ◽  
Damage Assessment ◽  
Assessment Tool ◽  
Building Damage ◽  
Damage Survey ◽  
Simple Tool ◽  
Similar Survey ◽  
Collapsed Buildings ◽  
Damage Condition ◽  
Post Disaster

Following a damaging earthquake, the immediate emergency response is focused on individual collapsed buildings or other “hotspots” rather than the overall state of damage. This lack of attention to the global damage condition of the affected region can lead to the reporting of misinformation and generate confusion, causing difficulties when attempting to determine the level of post-disaster resources required. A pre-planned building damage survey based on the transect method is recommended as a simple tool to generate an estimate of the overall level of building damage in a city or region. A methodology for such a transect survey is suggested, and an example of a similar survey conducted in Christchurch, New Zealand, following the 22 February 2011 earthquake is presented. The transect was found to give suitably accurate estimates of building damage at a time when information was keenly sought by government authorities and the general public.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

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.

scholarly journals Deep Learning-Based Identification of Collapsed, Non-Collapsed and Blue Tarp-Covered Buildings from Post-Disaster Aerial Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1924 ◽  
Author(s):  
Hiroyuki Miura ◽  
Tomohiro Aridome ◽  
Masashi Matsuoka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Building Damage ◽  
Aerial Images ◽  
Automated Identification ◽  
Damage Data ◽  
Collapsed Buildings ◽  
Post Disaster ◽  
Earthquake Data

A methodology for the automated identification of building damage from post-disaster aerial images was developed based on convolutional neural network (CNN) and building damage inventories. The aerial images and the building damage data obtained in the 2016 Kumamoto, and the 1995 Kobe, Japan earthquakes were analyzed. Since the roofs of many moderately damaged houses are covered with blue tarps immediately after disasters, not only collapsed and non-collapsed buildings but also the buildings covered with blue tarps were identified by the proposed method. The CNN architecture developed in this study correctly classifies the building damage with the accuracy of approximately 95 % in both earthquake data. We applied the developed CNN model to aerial images in Chiba, Japan, damaged by the typhoon in September 2019. The result shows that more than 90 % of the building damage are correctly classified by the CNN model.

scholarly journals Temporary Seismic Stations Installation and Building Damage Assessment of December 7th, 2016 Earthquake in Pidie Jaya, Aceh, Indonesia

2018 ◽  
Vol 16 (1) ◽  
pp. 15
Author(s):  
Zulfakriza Z. ◽  
Andri D. Nugraha ◽  
M. Ridwan ◽  
Kadek P. Hendrawan ◽  
Muksin Umar ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Building Damage ◽  
Earthquake Source ◽  
The Other ◽  
Alluvial Deposits ◽  
Damage Survey ◽  
Seismic Stations ◽  
Other Hand

A signicant Mw 6.5 earthquake occurred in Pidie Jaya, Aceh on December 7th, 2016. The event affected104 people death and more than 1000 people suered injuries due to the rubble of the building. Geologically, the region is composed by of Quaternary alluvial deposits. This is one of factor that amplication occurred in some area. On the other hand, an understanding of the source and mechanism of the earthquake needs to be done. A few days after the earthquake, we deployed 9 seismometers that covered the area of Pidie, Pidie Jaya and Bireuen. This experiment aims to record the aftershock and understanding of earthquake source and mechanism. In addition, we conducted building damage survey to know the pattern of distributionof building damage.

scholarly journals DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA

2021 ◽  
Vol V-1-2021 ◽  
pp. 113-120
Author(s):  
A. Calantropio ◽  
F. Chiabrando ◽  
M. Codastefano ◽  
E. Bourke
Keyword(s):  
Deep Learning ◽  
Damage Assessment ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Building Damage ◽  
Research Field ◽  
Future Research ◽  
Damage Classification ◽  
Research Perspectives ◽  
Post Disaster

Abstract. During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.

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.

GIS-Based Damage Certification Support System Based on Recent Earthquake Experience

2010 ◽  
Vol 5 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Katsuyuki Matsuoka ◽  
◽  
Haruo Hayashi ◽  
Nozomu Yoshitomi ◽  
Go Urakawa ◽  
...  
Keyword(s):  
Support System ◽  
Damage Assessment ◽  
Disaster Recovery ◽  
Research Team ◽  
Support Systems ◽  
Building Damage ◽  
Post Disaster ◽  
Assessment Results

During the 2004 Niigata Chuetsu Earthquake in Ojiya City, and 2007 Niigata Chuetsu-oki Earthquake in Kashiwazaki City, our research team built databases of building damage assessment results based on geographical coordinates and damage certification support systems (DCSS) for issuing damage certificates required by Japanesemunicipalities providing citizens with post-disaster recovery assistance. This paper discusses four major issues on databases and DCSS development, together with measures for solving these issues.

scholarly journals PROTOTYPING A SYSTEM OF AN EARLY BUILDING DAMAGE ASSESSMENT EFFECTED BY AN EARTHQUAKE

EXPLORE ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wahyu Ramadhan ◽  
Jamaludin
Keyword(s):  
Information System ◽  
Damage Assessment ◽  
Assessment Tool ◽  
Building Damage ◽  
Design Development ◽  
The Government ◽  
Addie Model ◽  
Set Up ◽  
A Current ◽  
Observation Results

The main purpose of this research is to identify a level of a building damage caused by an earthquake early and in detail by utilizing an information system. Early identification of the building damage will be categorized based on three categories, namely minor damage, moderate damage and major damage. Each classification has standards and calculations set up by the government. To find out the damage categories, a current study was carried out on how to develop an information system that could be used by evaluators in determining conclusions according to observation results. Related to the research and development method, and adapting the Analysis, Design, Development, Implementation and Evaluation (ADDIE) development model, researchers collaborate between the ADDIE model and the standard building damage values decided by the government in a real time. The design of the early damage assessment tool for buildings is still under development and is still a prototype system.Keywords:         Buliding Damage, Information System, ADDIE

Applications of Drone for Building Damage Survey in a Disaster

2019 ◽  
Vol 57 (9) ◽  
pp. 733-742
Author(s):  
Y. Maida ◽  
T. Mukai ◽  
H. Miyauchi
Keyword(s):  
Building Damage ◽  
Damage Survey

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.

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