scholarly journals Earthquake Damage Assessment Based on Deep Learning Change Detection Method Using VHR Images

2020 ◽  
Author(s):  
Reza Shah-Hosseini ◽  
Masoud Moradi
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Damage Assessment ◽  
Detection Method ◽  
Earthquake Damage ◽  
Earthquake Damage Assessment

The September 7, 2017 Tehuantepec, Mexico, earthquake: Damage assessment in masonry structures for housing

2021 ◽  
Vol 56 ◽  
pp. 102123
Author(s):  
Eber Alberto Godínez-Domínguez ◽  
Arturo Tena-Colunga ◽  
Luis Eduardo Pérez-Rocha ◽  
Hans Israel Archundia-Aranda ◽  
Alonso Gómez-Bernal ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Earthquake Damage ◽  
Masonry Structures ◽  
Mexico Earthquake ◽  
Earthquake Damage Assessment

scholarly journals A fuzzy decision making system for building damage map creation using high resolution satellite imagery

2013 ◽  
Vol 13 (2) ◽  
pp. 455-472 ◽  
Author(s):  
H. Rastiveis ◽  
F. Samadzadegan ◽  
P. Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Damage Assessment ◽  
Fuzzy Inference ◽  
Earthquake Damage ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
High Resolution Satellite Imagery ◽  
Roof Part ◽  
Earthquake Damage Assessment

Abstract. Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings.

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

scholarly journals Crowdsourcing earthquake damage assessment using remote sensing imagery

2012 ◽  
Vol 54 (6) ◽  
Author(s):  
Luke Barrington ◽  
Shubharoop Ghosh ◽  
Marjorie Greene ◽  
Shay Har-Noy ◽  
Jay Berger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Damage Assessment ◽  
Earthquake Damage ◽  
Remote Sensing Imagery ◽  
Earthquake Damage Assessment

scholarly journals COHERENT CHANGE DETECTION FOR REPEATED-PASS INTERFEROMETRIC SAR IMAGES: AN APPLICATION TO EARTHQUAKE DAMAGE ASSESSMENT ON BUILDINGS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 383-388 ◽  
Author(s):  
D. Oxoli ◽  
P. Boccardo ◽  
M. A. Brovelli ◽  
M. E. Molinari ◽  
A. Monti Guarnieri
Keyword(s):  
Change Detection ◽  
Emergency Management ◽  
Performance Assessment ◽  
Damage Assessment ◽  
Earthquake Damage ◽  
Detection Sensitivity ◽  
Management Service ◽  
Sar Images ◽  
Coherent Change Detection ◽  
Interferometric Sar

<p><strong>Abstract.</strong> During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Ametrine and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service &amp;ndash; European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts.</p>

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