Visual Damage Assessment using High-Resolution Satellite Images following the 2003 Bam, Iran, Earthquake

Saito, Keiko; Spence, Robin; Foley, Terence A. de C

doi:10.1193/1.2101107

Cited by 26 publications

(12 citation statements)

References 3 publications

(2 reference statements)

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“…Multi-source RS data, such as oblique images, can provide more views of buildings on the ground. Multi-temporal RS data, such as pre-earthquake images, are considered to be very useful as a reference in identifying the damage in the post-event image [4]. Although these adjustments will refine the final results towards rapid damage assessment, a problem should be considered: how to balance the operational complexity of system and the improvement of results, because ordinary people are more inclined to use easy-to-operate systems without spending too much time.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

et al. 2016

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Abstract:Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes' theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and "ground truth" according to multiple participants' assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring "ground truth" according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

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Section: Discussionmentioning

confidence: 99%

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

et al. 2016

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“…Booth et al [6] used vertical aerial images, Pictometry images, and ground observations to assess building damage in the 2011 Haitian earthquake. Building by building visual damage interpretation [7] based on the European Macroseismic Scale (EMS-98) [8] was carried out in a case study of the Bam earthquake. Huyck et al [9] used multisensor optical satellite imagery to map citywide damage with neighborhood edge dissimilarities.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression

Liu

Wang

2019

Remote Sensing

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We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. a set of 13 change detention features and support vector machine (SVM). Simon Plank [12] reviewed the methods of rapid damage assessment using multitemporal Synthetic Aperture Radar(SAR) data. Gupta et al. [13] present a satellite imagery dataset for building damage assessment with over 700,000 labeled building instances covering over 5000 km 2 of imagery.Recent studies show that the machine learning algorithm performs well in earthquake damage assessment. Li [14] assessed building damage with one-class SVM using pre-and post-earthquake QuickBird imagery and assessed the discrimination power of different level (pixel-level, texture, and object-based) features. Haiyang et al. [15] combined SVM and the image segmentation method to detect building damage. Cooner et al. [16] evaluate the effectiveness of machine learning algorithms in detecting earthquake damage. A series of textural and structural features were used in this study. A SVM and feature selection approach was carried out for damage mapping with post-event very high spatial resolution(VHR) image and obtained overall accuracy (OA) of 96.8% and Kappa of 0.5240 [11]. Convolutional neural networks (CNN) was utilized to identify collapsed buildings from post-event satellite imagery and obtained an OA of 80.1% and Kappa of 0.46 [17]. Multiresolution feature maps were derived and fused with CNN for the image classification of building damages in [18], and an OA of 88.7% was obtained.Most of the above-mentioned damage information extracti...

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“…The conventional technologies used in passive optical satellites operate mostly in the visible and infrared spectrum and are mainly used to map damage by visual interpretation [6][7][8] and to assess the vegetation status via the normalized difference vegetation index (NDVI). Conversely, synthetic aperture radar (SAR) is an active microwave sensor with day/night and all-weather operational capabilities that is highly sensitive to surface roughness, geometrical structures (e.g., built-up areas) and canopy volume in vegetated areas [9].…”

Section: Introductionmentioning

confidence: 99%

Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments

et al. 2018

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Rapid damage mapping following a disaster event, especially in an urban environment, is critical to ensure that the emergency response in the affected area is rapid and efficient. This work presents a new method for mapping damage assessment in urban environments. Based on combining SAR and optical data, the method is applicable as support during initial emergency planning and rescue operations. The study focuses on the urban areas affected by the Tohoku earthquake and subsequent tsunami event in Japan that occurred on 11 March 2011. High-resolution TerraSAR-X (TSX) images of before and after the event, and a Landsat 5 image before the event were acquired. The affected areas were analyzed with the SAR data using only one interferometric SAR (InSAR) coherence map. To increase the damage mapping accuracy, the normalized difference vegetation index (NDVI) was applied. The generated map, with a grid size of 50 m, provides a quantitative assessment of the nature and distribution of the damage. The damage mapping shows detailed information about the affected area, with high overall accuracy (89%), and high Kappa coefficient (82%) and, as expected, it shows total destruction along the coastline compared to the inland region.

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Visual Damage Assessment using High-Resolution Satellite Images following the 2003 Bam, Iran, Earthquake

Cited by 26 publications

References 3 publications

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression

Combining TerraSAR-X and Landsat Images for Emergency Response in Urban Environments

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