Building Damage Detection Research Articles

AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL

Abstract. In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

Building Damage Detection Using Object-Based Image Analysis and ANFIS From High-Resolution Image (Case Study: BAM Earthquake, Iran)

Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel- and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit ( $\text {rect\_fit}$ ), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.

Rapid three-dimensional detection approach for building damage due to earthquakes by the use of parallel processing of unmanned aerial vehicle imagery

Rapid damage assessment after an earthquake is vital for an efficient emergency response. With the rapid development of unmanned aerial vehicles (UAVs), they can now be used to rapidly assess the building damage and have the advantages of real-time operation, flexibility, low cost, etc. However, UAV images are “big data,” and UAVs can obtain hundreds of scene images in a short period of time. It is, therefore, important to speed up the processing time for UAV images. This paper proposes a parallel processing approach for accelerating the speed of automatic three-dimensional (3-D) building damage detection, using a preseismic digital topographical map and postseismic UAV images. From the experimental results obtained from 3-D building damage detection of the 2013 Ya’an earthquake in Baoxing County, Sichuan province of China, the comparison of the parallel processing in terms of digital surface model generation using postseismic UAV images shows that the total cost of the multicores central processing units (CPUs) and graphics processing unit-based implementation is about 11.0 times faster than the single-core CPU implementation.

Time‐wave velocity analysis for early earthquake damage detection in buildings: application to a damaged full‐scale RC building

SummaryAn algorithm for time velocity analysis of building response is presented, which identifies the wave velocity of vertically propagating waves through the building and detects their changes. The algorithm is intended for use in SHM systems for rapid assessment of the structural health and integrity following an earthquake. The velocities are identified by an interferometric algorithm, which involves least squares fit of pulses in impulse response functions. An important feature of this method is that it is not sensitive to the effects of soil–structure interaction. The algorithm is applied to a 12‐story RC building in Los Angeles, lightly damaged by the San Fernando 1971 earthquake. The results of the time velocity analysis are critically compared with results of analysis of input power, interstory drift, and instantaneous frequency. The identified average vertical wave velocity was initially ~140 m/s for the NS and ~110 m/s for the EW response and reduced by ~26% and 32%, respectively. The detected reduction of the fundamental frequency of vibration was larger (~44% for the NS and 48% for the EW response). The difference is interpreted to be due to softening of the soil–foundation system, to which the identified frequency of vibration is sensitive. The change was larger in the lower half of the building (~31% for the NS and ~37% for the EW response) as compared with the upper half (~24% for the NS and ~27% for the EW response), consistent with the observed damage. Copyright © 2015 John Wiley & Sons, Ltd.

撮影方向の異なる高分解能SAR画像を用いた建物被害の検出精度の向上に関する研究

高分解能SAR画像による建物被害の検出精度の向上を目指して, 2010年ハイチ地震の被災地を対象として, 撮影方向が異なる2ペアの地震前後のSAR画像を用いて, 個々の建物の倒壊の有無を判別する解析を行った．まず, 撮影方向がほぼ同一の1ペアの画像と建物フットプリントデータを用いて, 地震前後の強度差分値と相関係数に基づく線形判別解析から, それぞれの検出精度を比較したところ, 画像の撮影方向と建物の立地方向の関係によって精度が変化することを明らかにした．さらに, 2ペアの画像を用いた判別解析を行い, 1ペアの画像解析に比べて検出精度がやや向上することを示した．

Damage Detection in High-Rise Buildings Using Damage-Induced Rotations

In this paper, a new damage-detection method based on structural vibration is proposed. The essence of the proposed method is the detection of abrupt changes in rotation. Damage-induced rotation (DIR), which is determined from the modal flexibility of the structure, initially occurs only at a specific damaged location. Therefore, damage can be localized by evaluating abrupt changes in rotation. We conducted numerical simulations of two damage scenarios using a 10-story cantilever-type building model. Measurement noise was also considered in the simulation. We compared the sensitivity of the proposed method to localize damage to that of two conventional modal-flexibility-based damage-detection methods, i.e., uniform load surface (ULS) and ULS curvature. The proposed method was able to localize damage in both damage scenarios for cantilever structures, but the conventional methods could not.

A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images

Abstract. In this paper, a novel approach is presented that applies multiple overlapping UAV imagesto building damage detection. Traditional building damage detection method focus on 2D changes detection (i.e., those only in image appearance), whereas the 2D information delivered by the images is often not sufficient and accurate when dealing with building damage detection. Therefore the detection of building damage in 3D feature of scenes is desired. The key idea of 3D building damage detection is the 3D Change Detection using 3D point cloud obtained from aerial images through Structure from motion (SFM) techniques. The approach of building damage detection discussed in this paper not only uses the height changes of 3D feature of scene but also utilizes the image's shape and texture feature. Therefore, this method fully combines the 2D and 3D information of the real world to detect the building damage. The results, tested through field study, demonstrate that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and suited well for rapid damage assessment after natural disasters.

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An attempt of using straight-line information for building damage detection based only on post-earthquake optical imagery

It is important to grasp damage information in stricken areas after an earthquake in order to perform quick rescue and recovery activities. Recent research into remote sensing techniques has shown significant ability to generate quality damage information. The methods based on only post-earthquake data are widely researched especially because there are no pre-earthquake reference data in many cities of the world. This paper addresses a method for detection of damaged buildings using only post-event satellite imagery so that scientists and researchers can take advantage of the ability of helicopters and airplanes to fly over the damage faster. Statistical information of line segments extracted from post-event satellite imagery, such as mean length (ML) and weighted tilt angel standard deviation (WTASD), are used for discriminating the damaged and undamaged buildings.

Towards a rapid automatic detection of building damage using remote sensing for disaster management

Purpose– Satellite and airborne images are increasingly used at different stages of disaster management, especially in the detection of infrastructure damage. Although semi- or full automatic techniques to detect damage have been proposed, they have not been used in emergency situations. Damage maps produced by international organisations are still based on visual interpretation of images, which is time- and labour-consuming. The purpose of this paper is to investigate how an automatic mapping of damage can be helpful for a first and rapid assessment of building damage.Design/methodology/approach– The study area is located in Port-au-Prince (Haiti) stricken by an earthquake in January 2010. To detect building damage, the paper uses optical images (15 cm of spatial resolution) coupled with height data (LiDAR, 1 m of spatial resolution). By undertaking an automatic object-oriented classification, the paper identifies three categories of building damages: intact buildings, collapsed buildings and debris.Findings– Data processing for the study area covering 11 km2 took about 15 hours. The accuracy of the classification varies from 70 to 79 per cent depending to the methods of assessment. Causes of errors are numerous: limited spectral information of the optical images, resolution difference between the two data, high density of buildings but most importantly, certain types of building collapses could not be detected by vertically taken images (the case of data in this study).Originality/value– The automatic damage mapping developed in this paper proves to be reliable and could be used in emergency situations. It could also be combined with manual visual interpretation to accelerate the planning of humanitarian rescues and reconstruction.

Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection

This paper compares the results of building damage detection based on Crowd Sourced (CS) data, image processing of remotely sensed (RS) data and predictive modelling with institutional spatial data (Spatial Data Infrastructure - SDI). In particular, it focuses on the contribution of Crowd Sourcing to detecting post-earthquake building damages, while also considering the integration of Crowd Sourced with two other data sources (RS and modelling). To simulate CS data submission following the 2003 earthquake in Bam City (Iran) a survey was administered to the population which experienced the earthquake. The results obtained from this and two other sources are compared with the Actual Earthquake (AE) data by cross-tabulation analysis and McNemar's Chi Square Test. When assessed against AE data, the average accuracy levels of assessments based on the use of RS data and CS data integrated with each RS data and predictive modelling and with both, show a statistically significant increase relative to the predictive modelling. While this research does not provide for a full assessment of the value of CS data alone and in fact finds it slightly inferior to predictive modelling, it suggests that Crowd Sourcing could be a useful source of information, especially if combined with other sources.

Damage Detection Using High-Resolution SAR Imagery in the 2009 L'Aquila, Italy, Earthquake

Satellite remote sensing is being used to monitor disaster-affected areas for post-disaster reconnaissance and recovery. One of the special features of Synthetic Aperture Radar (SAR) is that it can operate day and night and penetrate the cloud cover because of which it is being widely used in emergency situations. Building damage detection for the 6 April 2009 L'Aquila, Italy, earthquake was conducted using high-resolution TerraSAR-X images obtained before and after the event. The correlation coefficient and the difference of backscatter coefficients of the pre- and post-event images were calculated in a similar way as Matsuoka and Yamazaki (2004) . The threshold value of the correlation coefficient was suggested and used in detecting building damage. The results were compared with ground truth data and a post-event optical image. Based on the study, building damage could be observed in an urban setting of L'Aquila with overall accuracy of 89.8% and Kappa coefficient of 0.45.

Evaluation of Three-Dimensional Shape Signatures for Automated Assessment of Post-Earthquake Building Damage

This paper proposes two new measures of three-dimensional shape signatures based on the slope and aspect of three-dimensional triangles, and evaluates the performance of three-dimensional shape signatures derived from distance, area, angle, volume, slope, and aspect for assessing post-earthquake building damage using simulated building models with flat, pent, gable and hip roofs. Three scenarios of post-earthquake building damage are tested using simulated light detection and ranging (LiDAR) points generated on the building roofs. Dissimilarity, sensitivity, and computational cost of the three-dimensional shape signatures are also discussed. The results show that a combination of three-dimensional shape signatures derived from slope and aspect can provide better detection of post-earthquake building damage compared with those derived from distance, area, angle, and volume. The paper demonstrates a promising method for rapid assessment of post-earthquake building damage using existing three-dimensional urban models and post-earthquake LiDAR data.

Use of non-destructive techniques in Chinese traditional timber structures

The study of structural damage detection in ancient Chinese timber buildings is very important in the protection and rehabilitation of the ancient architectural culture. The purpose of structural damage detection is to identify the location and grade of structural damage based on the variation in characteristics of the structure, which is dependent on positioning techniques and quantitative analysis of structural damage detection. These elements are studied thoroughly by introducing microtremor measurements and using the finite-element method. Multi-domain microtremor measurements are performed to detect the tilting and twisting of the structure. From the observational data, the natural frequencies and vibration modes of the structure are evaluated, and these are compared with numerical results. In the numerical analysis, the changes in dynamic characteristics of the structure, such as natural frequencies and natural modes, are obtained, with special attention paid to the characteristics of brackets. Comparing the natural frequencies and natural modes from microtremor measurements with those from three-dimensional numerical analysis, the location and form of the damage is qualitatively delimited. Through continued reduction of the stiffness of the damaged layer in different directions, until the natural frequencies and natural modes from the simulation are shown to be consistent with those of the microtremor measurements, the severity of damage to the structure is quantitatively evaluated.

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Detection of Building Damage Areas of the 2006 Central Java, Indonesia, Earthquake through Digital Analysis of Optical Satellite Images

In order to evaluate the capability of building damage detection from optical satellite images, a procedure for digital image analysis is examined and applied to images captured before and after the 2006 Central Java, Indonesia, earthquake. In the image analysis, the pixels of the images are classified into vegetation, bare ground, and built-up areas. The damage areas are detected by the differential of the digital numbers in the built-up areas. The estimated damage distribution is validated by comparing it with the GIS data on building damage obtained from a field survey. The results show that the severely damaged areas were well detected by the analysis. In the densely vegetated areas, however, the damage was underestimated because many of the buildings were obscured by trees. For assessing quantitative damage information, the relationship between the number of collapsed buildings and the areas detected by the image analysis is evaluated.

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