Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies

Menderes, Aydan; Erener, Arzu; Sarp, Gulcan

doi:10.1016/j.proeps.2015.08.063

Cited by 50 publications

(22 citation statements)

References 14 publications

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“…Therefore, remote sensing has been widely used in various post-disaster rescue operations, being particularly important for earthquake-stricken areas where it is often difficult to conduct field surveys 2 of 16 for the first time [3]. To some extent, previous studies have proved that relatively accurate information of building damage can be obtained from remote sensing data [4].…”

Section: Introductionmentioning

confidence: 99%

Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

Liu

Ren

et al. 2020

Remote Sensing

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Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images.

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Section: Introductionmentioning

confidence: 99%

Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

Liu

Ren

et al. 2020

Remote Sensing

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“…With progress in sensor and space technologies, remote sensing techniques can acquire detailed spatial and temporal information of the target area, which is widely used to monitor natural disasters [3,4]. Previous studies have proven that remote sensing data can extract relatively accurate information on building damage [5]. of the samples, and adopted the CNN framework based on the residual connection and expansion convolution to improve the classification effect.…”

Section: Introductionmentioning

confidence: 99%

Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

et al. 2019

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An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model's generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.For the extraction of information on building damage from remote sensing images, previous studies have investigated numerous methods, which can currently be divided into multi-and single-temporal evaluation methods. The multi-temporal evaluation method is mainly based on detecting changes to evaluate the information on building damage. Gong et al. [6] used high-resolution remote sensing images from before and after the 2010 Yushu earthquake as examples for the extraction of information on building damage based on the object-oriented change detection, pixel-based change detection, and principal component analysis-based change detection methods. The results showed that the object-oriented change detection method had the highest accuracy for extracting information on building damage. However, due to effects from data acquisition, such as revisit cycles, shooting angle, time, and other factors, the application of the multi-temporal evaluation method is difficult in practice [7]. For the single-temporal evaluation method, data acquired via remote sensing after an earthquake has less constraints, such that it has become an effective technical means that can be directly used to extract and evaluate information on building damage [8]. Janalipour et al. [9] used high spatial resolution remote sensing images as backg...

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“…The information that is typically obtained from structural monitoring tools, in this regard, is of fundamental importance in view of the consequences associated to possible collapse phenomena. Those systems provide in fact the authorities with a careful evaluation of the damage evolution, supporting the planning of the restoration interventions (e.g., [2][3][4][5], etc.). Structural Health Monitoring (SHM) and non-destructive testing have key roles for structural systems in operational conditions, for monumental buildings ( [6][7][8], etc.…”

Section: Introduction State-of-the-art and Objectivesmentioning

confidence: 99%

Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

Bedon

Bergamo

Izzi

et al. 2018

JSAN

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In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

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Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies

Cited by 50 publications

References 14 publications

Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

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