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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.

A methodology for the automated identification of building damage from post-disaster aerial images was developed based on convolutional neural network (CNN) and building damage inventories. The aerial images and the building damage data obtained in the 2016 Kumamoto, and the 1995 Kobe, Japan earthquakes were analyzed. Since the roofs of many moderately damaged houses are covered with blue tarps immediately after disasters, not only collapsed and non-collapsed buildings but also the buildings covered with blue tarps were identified by the proposed method. The CNN architecture developed in this study correctly classifies the building damage with the accuracy of approximately 95 % in both earthquake data. We applied the developed CNN model to aerial images in Chiba, Japan, damaged by the typhoon in September 2019. The result shows that more than 90 % of the building damage are correctly classified by the CNN model.

The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.

In Indonesia, tsunamis are frequent events. In 2000-2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas.

This study develops a novel approach for identifying buildings that were damaged in the aftermath of the Kahramanmaraş earthquakes on February 6, 2023, which were among the most devastating in the history of Türkiye. The approach involves using two pre-event and one post-event Sentinel-1 and Sentinel-2 images to detect changes in the varying-sized and shaped buildings following the earthquakes. The approach is based on the hypothesis that the radiometric characteristics of building pixels should change after an earthquake, and these changes can be detected by analysing the spectral distance between the building pixel vectors before and after the earthquake. The proposed approach examines the changes in building pixel vectors on pre-event and post-event Sentinel-2 MultiSpectral Instrument images. It also incorporates the backscattering features of Sentinel-1 Synthetic Aperture Radar images, as well as the variance image, a feature that is derived from a Grey-Level Co-occurrence Matrix, and the Normalized Difference Built-up Index image, which were derived from the optical data. The approach was tested on three sites, two of which were in Kahramanmaraş and the third in Hatay city. The results showed that the proposed method was able to accurately identify damaged and undamaged buildings with an overall accuracy of 75%, 84.4%, and 73.8% in test sites 1, 2, and 3, respectively. These findings demonstrate the potential of the proposed approach to effectively identify damaged buildings in post-disaster situations.

Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately assess the damage characteristics. The buildings identified as Major damage in the Japanese damage certification survey contain damage with various characteristics. If Major damage is treated as a single class, the parameters of SAR images will vary greatly, and the relationship between building damage and SAR images would not be properly evaluated. Therefore, it is necessary to divide Major damage buildings into more detailed classes. In this study, the Major damage buildings were newly classified into five damage classes, to correctly evaluate the relationship between building damage characteristics and SAR imagery. The proposed damage classification is based on Japanese damage assessment data and field photographs, and is classified according to the dominant damage characteristics of the building, such as collapse and damage to walls and roofs. We then analyzed the backscattering characteristics of SAR images for each classified damage class. We used ALOS-2 PALSAR-2 images observed before and after the 2016 Kumamoto earthquake in Mashiki Town, where many buildings were damaged by the earthquake. Then, we performed the analysis using two indices, the correlation coefficient R and the coherence differential value γdif, and the damage class. The results indicate that the backscattering characteristics of SAR images show different trends in each damage class. The R tended to decrease for large deformations such as collapsed buildings. The γdif was likely to be sensitive not only to collapsed buildings but also to damage with relatively small deformation, such as distortion and tilting. In addition, it was suggested that the ground displacement near the earthquake fault affected the coherence values.

On the 26th of November 2019, an earthquake of moment magnitude 6.4 struck the northwest region of Albania as the result of thrust faulting near the convergent boundary of the Africa and Eurasia plates causing widespread damage to buildings in the city of Durrës and the surrounding areas. Based on the official data from the national authorities, the earthquake caused 51 casualties and 985 million-euro losses, corresponding to 7.5% of the 2018 gross domestic product. This paper summarises field observations made by the Earthquake Engineering Field Investigation Team (EEFIT) after the event. The paper presents an overview of the seismological aspects of the earthquake together with a brief overview of the damage, official loss statistics and the estimated macro- and socio-economic consequences of the event. In addition, it provides a summary of the observed damage to both recent and historical buildings as well as the description of several case studies to illustrate the characteristic damage patterns observed in the main structural typologies of the Albanian building stock. These observations try to identify possible links between the observed damage patterns and the deficiencies in construction practice and use of inappropriate retrofit techniques for historical assets. As many severe damages were observed on modern buildings, this also allows the identification of some gaps and possible areas of development of the current seismic design code. In the end, the lessons learned from the field survey are resumed.

On 24 August 2016, the M 6.2 Amatrice earthquake struck central Italy, well-known as a seismically active region, causing considerable damage to buildings in the town of Amatrice and the surrounding area. Damage from this earthquake was assessed quantitatively by means of multitemporal synthetic aperture radar (SAR) coherence and SAR intensity methods using dual-polarized SAR data obtained from the Sentinel-1 (VV, VH) and ALOS-2 (HH, HV) satellites. We developed linear discriminant functions based on three items: (1) the differential coherence values; (2) the differential backscattering intensity values of pre- and post-event images; and (3) a binary damage map of the optical pre- and post-event imagery. The accuracy of the proposed model was 84% for the Sentinel-1 data and 76% for the ALOS-2 data. The damage proxy maps deduced from the linear discriminant functions can be useful in the parcel-by-parcel assessment of building damage and development of spatial models for the allocation of urban search and rescue operations.

Timely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on bi-temporal pre/post-earthquake dataset extraction information from bi-temporal images, which is difficult. Furthermore, these methods require manual feature engineering for supervised learning models. To tackle the abovementioned limitation of the traditional damage detection frameworks, this research proposes a novel building damage map generation approach based only on post-event RS satellite imagery and advanced deep feature extractor layers. The proposed DL based framework is applied in an end-to-end manner without additional processing. This method can be conducted in five main steps: (1) pre-processing, (2) model training and optimization of model parameters, (3) damage mapping generation, (4) accuracy assessment, and (5) visual explanations of the proposed method’s predictions. The performance of the proposed method is evaluated by two real-world RS datasets that include Haiti-earthquake and Bata-explosion. Results of damage mapping show that the proposed method is highly efficient, yielding an OA of more than 84%, which is superior to other advanced DL-based damage detection methods.

Damage assessment from very high resolution (VHR) remote sensing imagery plays a fundamental role in the delineation of the impact caused by catastrophic events. To date internationally accepted standard guidelines on how to assess damages to building using vertical imagery have not yet been developed. This study therefore proposes a building damage scale - and related interpretation guidelines to be operationally adopted as a standard by the main stakeholders - tailored to analyses based on VHR remote sensed vertical imagery. Preliminarily, some of the damage scales used for building damage assessment by the main satellite-based emergency mapping services have been analysed and discussed. A quantitative thematic accuracy analysis based on the open accessible crisis datasets related to the earthquake occurred in Central Italy in August 2016 has been carried out. The results highlight that by using VHR remotely sensed images it is not possible to directly apply damage classification scales addressing slight structural damages (e.g. the lowest grades proposed by EMS-'98). The paper demonstrates that using different damage classes and detailing the interpretation guidelines with operational examples is essential to increase the thematic accuracy of the analysis.