Earthquake-induced Building Damage Research Articles

Multi-Scale Earthquake Damaged Building Feature Set

Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response.

Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, fine post-earthquake assessment results are needed to manage recovery and reconstruction and to estimate economic losses. In this paper, for quantification and precision purposes, a method of earthquake-induced building damage information extraction incorporating multi-source remote sensing data is proposed. The method consists of three steps： (1) Analysis of multisource features that describe texture, colour, and geometry, (2) rough set theory is carried out to further determine the feature parameters, (3) Logistic regression model (LRM) was built to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. Old Beichuan County (centered at approximately 31.833︒N, 104.459° E), China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Multi-source remote sensing imagery include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Through comparison with the ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potential of features for application in the damage detection field.

Quantitative assessment of earthquake-induced building damage at regional scale using LiDAR data

Regional-scale assessment of the damage caused by earthquakes to structures is crucial for post-disaster management. While remote sensing techniques can be of great help for a quick post-event structural assessment of large areas, currently available methods are limited to the detection of severely-damaged buildings. Furthermore, remote sensing-based assessment methods typically provide only qualitative results, as they lack integration with information on the building’s behaviour in response to seismic-induced ground shaking. In this study, we developed a new methodology that uses airborne Light Detection And Ranging (LiDAR) data in combination with structural indicators of building response to provide a quantitative assessment of earthquake-induced damage at a regional scale. LiDAR datasets collected before and after an earthquake are used to measure residual displacements of building roofs. The resulting lateral drift estimations are used to quantify the level of damage for a specific building typology. Application to the LiDAR datasets collected before and after the 2014 earthquake in Napa Valley, California, demonstrates the capability of the proposed method to detect moderate levels of structural damage, proving its potential for faster and more accurate support to post-disaster management.

Earthquake-induced building damage recognition from unmanned aerial vehicle remote sensing using scale-invariant feature transform characteristics and support vector machine classification

Building damage is the main cause of casualties and economic losses from earthquakes. Therefore, understanding building damage is critical for emergency handling. Current information acquisition methods for assessing earthquake damage using unmanned aerial vehicle (UAV) remote sensing systems offer great flexibility and high efficiency with the capability to obtain high-resolution images, which can reflect actual damage to affected areas intuitively. Consequently, UAV remote sensing has become a convenient and important means to acquire earthquake-induced building damage information. Although manual visual interpretation can achieve high recognition accuracy, it is extremely time-consuming. In contrast, although current automatic recognition methods require less time, they have relatively poor recognition accuracy. Neither approach can satisfactorily simultaneously meet efficiency and accuracy requirements for earthquake emergency handling. This article applies image classification algorithms based on the support vector machine (SVM) to earthquake-induced building damage recognition, and proposes a recognition method based on scale-invariant feature transform (SIFT) characteristics and SVM classification. We use the magnitude 6.4 earthquake at Yangbi (2021) as an example to validate the proposed method. Results verify that the proposed method can recognize earthquake damage quickly and accurately, providing effective support for decision-making regarding rescue actions.

Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine

Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine

Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level

• Superpixel-based direct change detection is capable of finding damages efficiently. • Extra feature enhancement bands accelerate the process of damage assessment. • Channel-expanded CNN model benefits simultaneous building damage analysis. • The potential of the proposed method in post-earthquake emergency is recognized. Accurate and quick building damage assessment is an indispensable step after a destructive earthquake. Acquiring building damage information of the seismic area in a remotely sensed way enables a timely emergency response. Existing remote sensing building damage detection methods based on convolutional neural network (CNN) mainly need two-step processing or only use single post-event image, leading to low efficiency and inaccurate building boundary. Considering the practical needs of emergency rescue and post-disaster reconstruction, this study proposed a hierarchical building damage assessment workflow using CNN-based direct remote sensing change detection on superpixel level. First, vulnerable building areas close to the epicenter are extracted using extra feature enhancement bands (EFEBs) to narrow the extent of image processing. Then, fine scale building damage is detected in the extracted building areas based on a direct change detection method with pre-event superpixel constraint (PreSC) strategy to improve the precision and efficiency. Finally, a rapid remote sensing earthquake damage index (rRSEDI) is used to quantitatively assess the damage. Experimental results of the case study show that damaged buildings can be effectively and accurately localized and classified using the proposed workflow. Comparative experiments with single-temporal image and post-event segmentation further embody the superiority of the direct change detection. The damage assessment result matches the official report after Ludian earthquake, proving the reliability of the proposed workflow. For future natural hazard events, the workflow can contribute to formulating appropriate disaster management, prevention and mitigation policies.

BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection

Building damage maps can be generated from either optical or Light Detection and Ranging (Lidar) datasets. In the wake of a disaster such as an earthquake, a timely and detailed map is a critical reference for disaster teams in order to plan and perform rescue and evacuation missions. Recent studies have shown that, instead of being used individually, optical and Lidar data can potentially be fused to obtain greater detail. In this study, we explore this fusion potential, which incorporates deep learning. The overall framework involves a novel End-to-End convolutional neural network (CNN) that performs building damage detection. Specifically, our building damage detection network (BDD-Net) utilizes three deep feature streams (through a multi-scale residual depth-wise convolution block) that are fused at different levels of the network. This is unlike other fusion networks that only perform fusion at the first and the last levels. The performance of BDD-Net is evaluated under three different phases, using optical and Lidar datasets for the 2010 Haiti Earthquake. The three main phases are: (1) data preprocessing and building footprint extraction based on building vector maps, (2) sample data preparation and data augmentation, and (3) model optimization and building damage map generation. The results of building damage detection in two scenarios show that fusing the optical and Lidar datasets significantly improves building damage map generation, with an overall accuracy (OA) greater than 88%.

An empirical method to estimate earthquake direct economic losses using building damages in high intensity area as a proxy

It is well known that earthquake brings about gigantic direct and indirect economic losses, such as building and lifeline system damages, every year at the global scales. Thus, efficient and accurately estimate the economic loss after earthquake is one of the important factors for the post-earthquake reconstruction and the recovery activity of ordinary life. To estimate the earthquake-induced direct economic losses (DEL), a regression empirical method based on the earthquake-induced direct building damage economic losses (DBDEL) is proposed in this study. Firstly, a linear model between DBDEL of the entire earthquake impact areas and the DBDEL of areas with earthquake intensity greater than VII are acquired based six historical earthquakes occurred at the mainland China. Then, another linear model between the DEL and the entire DBDEL are obtained based on 16 historical earthquakes. The DBDEL and DEL of the historical earthquake records are acquired from field investment. To evaluate the accuracy of the proposed method, the DEL of 2013 Lushan M 7.0 earthquake estimated. Using the field invested and high-resolution remote sensing image estimated DBDEL at the intensity greater than VII areas, the corresponding DELs are estimated based on the proposed method, respectively, with the relative error of 3.2% and 57.0%. In addition, an empirical DEL method based on earthquake magnitude and intensity is also used to estimate the Lushan earthquake DEL, with the relative error of 90.0%. The result indicates that the proposed method can properly estimate DEL based on the field invested or remote sensed DBDEL.

A Broad, Distributed Active Fault Zone Lies beneath Salt Lake City, Utah

Abstract Although the Wasatch fault is currently known to have a high-seismic hazard from motion along range-bounding faults, new seismic data reveal faulted and folded 13,000–30,000-yr-old Lake Bonneville strata beneath Salt Lake City (SLC). Coupled with previous excavation trench, borehole, and other geologic and geophysical observations, we conclude that a zone of latest Pleistocene and/or Holocene faulting and folding kinematically links the East Bench and Warm Springs faults through a 3 km wide relay structure and transfer zone. We characterize faults beneath downtown SLC as active, and these faults may displace or deform the ground surface during an earthquake. Through offset but linked faults, our observations support throughgoing ruptures across faults of the Wasatch fault zone (WFZ) and an elevated risk of earthquake-induced building damage.

Predicting building damages in mega-disasters under uncertainty: An improved Bayesian network learning approach

This paper develops a hybrid approach that integrates the cloud model and Bayesian networks (BNs) to predict building damages induced by earthquakes under uncertainty. The cloud model is used to perform data discretization for modeling information losses and uncertainties. The learned BN from the historical data by using the Peter-Clark (PC) algorithm is validated by using the k-fold cross-validation. Three types of analysis, including predictive, diagnosis, and sensitivity analysis, are conducted for new knowledge discovery. A selected dataset consisting of 9920 buildings in the 2015 Nepal earthquake is used to demonstrate the adaptability and significance of the proposed approach. Results imply that (1) The constructed BN displays a high precision in predicting earthquake-induced building damage, where the accuracy of the 10-fold cross-validation ranges from 0.89 to 0.92. (2) Building structures and foundation materials are identified as critical factors leading to severe building damages. (3) The proposed approach possesses better prediction capabilities than the conventional BN approach, where an average improvement of 3.5 % in terms of the average accuracy is achieved. This research provides a data-driven perspective to perceive the magnitude of building damages subjected to uncertainties and complexities.

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.

Classifying earthquake damage to buildings using machine learning

The ability to rapidly assess the spatial distribution and severity of building damage is essential to post-event emergency response and recovery. Visually identifying and classifying individual building damage requires significant time and personnel resources and can last for months after the event. This article evaluates the feasibility of using machine learning techniques such as discriminant analysis, k-nearest neighbors, decision trees, and random forests, to rapidly predict earthquake-induced building damage. Data from the 2014 South Napa earthquake are used for the study where building damage is classified based on the assigned Applied Technology Council (ATC)-20 tag (red, yellow, and green). Spectral acceleration at a period of 0.3 s, fault distance, and several building specific characteristics (e.g. age, floor area, presence of plan irregularity) are used as features or predictor variables for the machine learning models. A portion of the damage data from the Napa earthquake is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. It is noted that the random forest algorithm can accurately predict the assigned tags for 66% of the buildings in the test dataset.

Segment-by-segment comparison technique for earthquake-induced building damage map generation using satellite imagery

Known as an unpredictable natural disaster, earthquake is one of the most devastating natural disasters that causes significant life losses and damages, every year. After an earthquake, quick and accurate buildings damage identification for rescuing can reduce the number of fatalities. In this regard, Remote Sensing (RS) technology is an efficient tool for rapid monitoring of damaged buildings. This paper proposes a novel method, titled segment-by-segment comparison (SBSC), to generate buildings damage map using multi-temporal satellite images. The proposed method begins by extracting image-objects from pre- and post-earthquake images and equalizing them through segmentation intersection. After the extraction of various textural and spectral descriptors on pre- and post-event images, their differences are used as an input feature vector in a classification algorithm. Also, the Genetic Algorithm (GA) is used to find the optimum descriptors in the classification process. The accuracy of the proposed method was tested on two different datasets from different sensors. Comparing the damage maps obtained from the proposed method with the manually extracted damage map, above 92% of the buildings were correctly labelled in both datasets.

A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery

The accurate and quick derivation of the distribution of damaged building must be considered essential for the emergency response. With the success of deep learning, there is an increasing interest to apply it for earthquake-induced building damage mapping, and its performance has not been compared with conventional methods in detecting building damage after the earthquake. In the present study, the performance of grey-level co-occurrence matrix texture and convolutional neural network (CNN) features were comparatively evaluated with the random forest classifier. Pre- and post-event very high-resolution (VHR) remote sensing imagery were considered to identify collapsed buildings after the 2010 Haiti earthquake. Overall accuracy (OA), allocation disagreement (AD), quantity disagreement (QD), Kappa, user accuracy (UA), and producer accuracy (PA) were used as the evaluation metrics. The results showed that the CNN feature with random forest method had the best performance, achieving an OA of 87.6% and a total disagreement of 12.4%. CNNs have the potential to extract deep features for identifying collapsed buildings compared to the texture feature with random forest method by increasing Kappa from 61.7% to 69.5% and reducing the total disagreement from 16.6% to 14.1%. The accuracy for identifying buildings was improved by combining CNN features with random forest compared with the CNN approach. OA increased from 85.9% to 87.6%, and the total disagreement reduced from 14.1% to 12.4%. The results indicate that the learnt CNN features can outperform texture features for identifying collapsed buildings using VHR remotely sensed space imagery.

UAV and GIS based rapid earthquake-induced building damage assessment and methodology for EMS-98 isoseismal map drawing: The June 12, 2017 Mw 6.3 Lesvos (Northeastern Aegean, Greece) earthquake

On June 12, 2017, an Mw 6.3 earthquake struck Lesvos Island (Northeastern Aegean, Greece). Building damage was observed in its southeastern part with very heavy structural damage limited in the settlement of Vrissa. Taking into account that Vrissa is located further inland from the epicenter than other settlements with less damage, Vrissa looks like an earthquake impact paradox. For interpreting this paradox, a complete approach for damage assessment in an earthquake-affected area was applied during the first hours of the emergency response phase in order to provide crucial information to civil protection agencies. It comprises integration of building-by-building inspection, use of desktop and web GIS applications, UAV survey and digital post processing, extraction of data and information related to the buildings of the affected area, application of the European Macroseismic Scale 1998 and assignment of macroseismic intensities. Correlation of the all aforementioned data with the geological, geomorphological, geotechnical and seismological properties of the affected area along with its buildings characteristics was followed. This damage scene is attributed to the synergy of the near-field location of Vrissa, recent deposits, geotechnically unstable zones, proximity to active faults, rupture directivity phenomena and vulnerable buildings. The integration of UAV and web GIS applications during a rapid post-earthquake field macroseismic reconnaissance can potentially be considered as a methodological framework that can be applied for similar analysis in other areas affected not only by earthquakes but also by other extreme events that have the potential to cause destructive effects on the natural environment, humans and infrastructures.

Earthquake-Induced Building Damage Mapping Based on Multi-Task Deep Learning Framework

Earthquake-induced building damage can be directly devastating, leading to a large population loss and massive property damage with the high rate of globalization and urbanization. In case an earthquake takes place, the location and the scale of collapsed buildings are vital for rescuers to take effective aid measures immediately to reduce casualties and economic loss. The development in earth observation using high spatial resolution images makes it possible to recognize damaged and intact buildings. However, the methods proposed are most limited to specific cases with a limited number of buildings in pure background objects. In this paper, a multi-task deep learning framework is proposed to map damaged and intact buildings from large-scale very high spatial resolution images. The images used in our study have complicated background objects, which share similar spectral and textual characteristics with buildings. Therefore, we built a main task model to detect buildings in good shape and damaged conditions, and an extra task model to supplement the main task by semantically segmenting the input image into multiple ground objects in the multi-task framework. It is an end-to-end framework, comprising residual network and pyramid pooling module, aiming to extract multi-scale contextual features. The proposed framework is trained and evaluated in different parts of western Haiti, which gets affected by the earthquake in 2010. Besides, the results demonstrate that multi-task deep learning framework is encouraging to map earthquake-induced building damage in complicated background.

Synthetic building damage scenarios using empirical fragility functions: A case study of the 2016 Kumamoto earthquake

A methodology to create synthetic earthquake-induced building damage states for urban areas in Japan under specific earthquake events is presented in this paper. The methodology is based on empirical fragility curves for Japanese buildings, the building database of the study area and the strong motion magnitude spatial distribution. A stochastic approach was then used to allocate damage states to buildings. The synthetic building damage scenario is intended to be used as a platform for performing computational simulations of evacuation and relief distribution under multiple damage scenarios. The methodology is applied to an area within Mashiki town, Kumamoto Prefecture, which was affected by the Mw 7.0 2016 Kumamoto earthquake. The comparison of the synthetic buildings damage scenario with the surveyed data inventory shows good agreement in terms of the aggregate statistics.

Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake

Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.

Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images

Earthquake-induced building damage assessment is an indispensable prerequisite for disaster impact assessment, and the increasing availability of high-resolution Synthetic Aperture Radar (SAR) imagery has made it possible to construct damaged building inventories soon after earthquakes strike. However, the shortage of pre-seismic SAR datasets and the lack of available building footprint data pose challenges for rapid building damage assessment. Taking advantage of recent advances in machine learning algorithms, this study proposes an object-based building damage assessment methodology that uses only post-event SAR imagery. A Random Forest machine learning-based object classification, a simplified approach to the extraction of built-up areas, was developed and tested on two ALOS2/PALSAR-2 dual polarimetric SAR images acquired in affected areas soon after the 2015 Nepal earthquake. In addition, a series of texture metrics as well as the random scattering metric and reflection symmetry metric were found to significantly enhance classification accuracy. The feature selection was found to have a positive effect on overall performance. Moreover, the proposed Random Forest framework resulted in overall accuracies of 93% with a kappa coefficient of 0.885 when the object scale of 60 × 60 pixels and 15 features were adopted. A comparative experiment with the k-nearest neighbor framework demonstrated that the Random Forest framework is a significant step toward the achievement of a balanced, two-class classification.

PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH OBJECT-BASED IMAGE CLASSIFICATION

Abstract. Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (© Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.