Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake

Anniballe, Roberta; Noto, Fabrizio; Scalia, Tanya; Bignami, Christian; Stramondo, Salvatore; Chini, Marco; Pierdicca, Nazzareno

doi:10.1016/j.rse.2018.03.004

Cited by 66 publications

(33 citation statements)

References 29 publications

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“…Many different features have been introduced to determine building damage from remote sensing images [10]. Anniballe et al [11] investigated the capability of earthquake damage mapping at the scale of individual buildings with Remote Sens. 2019, 11, 2858 3 of 19 The main contributions of this paper are summarized as follows: (1) A deep ordinal regression network for assessing the degree of building damage caused by an earthquake.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

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confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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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“…The advantage of machine learning methods is that they can make full use of multidimensional features to achieve better accuracy. Consequently, the advanced change detection algorithms to identify the building damage are promoted, although these initiatives are represented by sophisticated procedures that are not suitable to implement in real disaster practice [17,18]. To make the delivered damage-mapping product more reliable and of a great reference value, a more generalized model should be developed.…”

Section: Introductionmentioning

confidence: 99%

“…To make the delivered damage-mapping product more reliable and of a great reference value, a more generalized model should be developed. For example, Anniballe et al (2018) proposed a method to assess the individual earthquake damaged buildings in a broad area using pre-event and a post-event very-high-resolution optical image. In this work, a straightforward supervised machine learning framework is proposed [17].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Anniballe et al (2018) proposed a method to assess the individual earthquake damaged buildings in a broad area using pre-event and a post-event very-high-resolution optical image. In this work, a straightforward supervised machine learning framework is proposed [17]. However, the method is a time-consuming high-dimensional feature extraction and selection procedure, which is not practical for disaster response considering the time cost.…”

Section: Introductionmentioning

confidence: 99%

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Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

2018

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The satellite remote-sensing-based damage-mapping technique has played an indispensable role in rapid disaster response practice, whereas the current disaster response practice remains subject to the low damage assessment accuracy and lag in timeliness, which dramatically reduces the significance and feasibility of extending the present method to practical operational applications. Therefore, a highly efficient and intelligent remote-sensing image-processing framework is urgently required to mitigate these challenges. In this article, a deep learning algorithm for the semantic segmentation of high-resolution remote-sensing images using the U-net convolutional network was proposed to map the damage rapidly. The algorithm was implemented within a Microsoft Cognitive Toolkit framework in the GeoAI platform provided by Microsoft. The study takes the 2011 Tohoku Earthquake-Tsunami as a case study, for which the pre- and post-disaster high-resolution WorldView-2 image is used. The performance of the proposed U-net model is compared with that of deep residual U-net. The comparison highlights the superiority U-net for tsunami damage mapping in this work. Our proposed method achieves the overall accuracy of 70.9% in classifying the damage into “washed away,” “collapsed,” and “survived” at the pixel level. In future disaster scenarios, our proposed model can generate the damage map in approximately 2–15 min when the preprocessed remote-sensing datasets are available. Our proposed damage-mapping framework has significantly improved the application value in operational disaster response practice by substantially reducing the manual operation steps required in the actual disaster response. Besides, the proposed framework is highly flexible to extend to other scenarios and various disaster types, which can accelerate operational disaster response practice.

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Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos

Marin

Artemov

et al. 2019

Advances in Neural Networks – ISNN 2019

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Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.

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Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake

Cited by 66 publications

References 29 publications

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

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

Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

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