CNN-based estimation of pre- and post-earthquake height models from single optical images for identification of collapsed buildings

Amirkolaee, Hamed Amini; Arefi, Hossein

doi:10.1080/2150704x.2019.1601277

Cited by 22 publications

(12 citation statements)

References 12 publications

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“…Deep learning technology has recently been widely used in VHR remote sensing image applications [43][44][45]. Lots of explorations in disaster assessments were performed in the literature [22,23,[25][26][27][28][29]42,[46][47][48][49][50][51][52][53][54]. Convolutional Neural Network (CNN) is a deep learning technique that can automatically learn the most effective features from samples while training.…”

Section: Introductionmentioning

confidence: 99%

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Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

2021

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The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

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Section: Introductionmentioning

confidence: 99%

“…Currently, some studies on CNN-based identification of damaged buildings for specific disaster events suggest certain accuracy [23,27,50]. However, there still exist some research gaps.…”

Section: Introductionmentioning

confidence: 99%

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

2021

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“…Their dataset included buildings but not specifically for buildings images. In Amini and Arefi [41], the authors presented a deep CNN network to detect the collapsed buildings after an earthquake using height estimation. They employed both RGB images and lidar data pre-event satellite image as well as post-event.…”

Section: Related Workmentioning

confidence: 99%

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Shahin

Almotairi

2021

Electronics

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Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

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“…Convolutional neural network (CNN) is a kind of feedforward neural network, which can learn and extract information from data layer by layer, and also can reveal the essential characteristics of the system hidden in data. It is widely used in the field of image processing [25]- [27]. The network is usually composed of convolution layer(C), pooling layer(P) and full connection layer(F).…”

Section: The Theory Of Convolutional Neural Networkmentioning

confidence: 99%

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

2020

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To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

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CNN-based estimation of pre- and post-earthquake height models from single optical images for identification of collapsed buildings

Cited by 22 publications

References 12 publications

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis

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