The rapid and accurate damage assessment of buildings plays a critical role in disaster response. Based on pairs of pre- and post-disaster remote sensing images, effective building damage level assessment can be conducted. However, most existing meth...
Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on pair...
Timely detection of the spatial distribution of building damage in the immediate aftermath of a disaster is essential for guiding emergency response and recovery strategies. In January 2025, a large-scale wildfire struck the Los Angeles metropolitan...
Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach go...
Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquak...
Natural disasters demand swift and accurate impact assessment, yet traditional field-based methods remain prohibitively slow. While semi-automatic techniques leveraging remote sensing and drone imagery have accelerated evaluations, existing datasets...
To characterize regional building-stock damage arising from the combined effects of spatially varying ground motions, building vulnerability, and exposure, this study develops a building-level, multi-scenario, and multi-record seismic damage assessme...
When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as inv...
Natural and anthropogenic disasters can cause significant damage to urban infrastructure, landscape, and loss of human life. Satellite based remote sensing plays a key role in rapid damage assessment, post-disaster reconnaissance and recovery. In thi...
The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tre...
Accurate building damage assessment from satellite imagery is essential for post-disaster response and recovery. Most deep learning approaches treat this task as semantic segmentation, producing pixel-level damage maps without separating individual b...
Moisture damage in buildings has traditionally been discussed primarily in relation to winter condensation in cold climates. In hot-humid regions, however, damage develops under different boundary conditions, including warm and humid outdoor air, fre...
Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit da...
Automated building damage assessment from satellite imagery has become increasingly critical for rapid disaster response and humanitarian relief operations. However, current state-of-the-art deep learning models exhibit significant generalization cha...
Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods ha...
Rapid and reliable building damage assessment (BDA) is crucial for post-disaster emergency response. However, existing methods face challenges such as complex background interference, the difficulty in jointly modeling local geometric details and glo...
Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level chan...
Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast de...
In the aftermath of a natural hazard, rapid and accurate building damage assessment from remote sensing imagery is crucial for disaster response and rescue operations. Although recent deep learning-based studies have made considerable improvements in...
Flood damage assessment (FDA) is an essential tool for evaluating flood damage, vulnerability, and risk to civil systems such as residential buildings. The outcome of an FDA depends on the spatial limits of the study and the complexity of the data. F...
The most negative effects caused by earthquakes are the damage and collapse of buildings. Seismic building retrofitting and repair can effectively reduce the negative impact on post-earthquake buildings. The priority to repair the construction after...
In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodol...
After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for...
The rapid assessment of building damage within a region after an earthquake is crucial for post-earthquake relief efforts. The current building damage assessment methods primarily employ remote sensing or structural equation modeling, which suffer fr...
During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human in...
Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results...
The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive a...
Earth Observation satellite imaging helps building diagnosis during a disaster. Several models are put forward on the xBD dataset, which can be divided into two levels: the building level and the pixel level. Models from two levels evolve into severa...
A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage...
Recent learning-based damage assessment methods using remote sensing data have greatly contributed to the rapid identification and classification of damaged buildings from pre- and post-disaster imagery. However, most outputs are provided in the form...
The rapid assessment of post-earthquake building damage for rescue and reconstruction is a crucial strategy to reduce the enormous number of human casualties and economic losses caused by earthquakes. Conventional machine learning (ML) approaches for...
When a natural disaster occurs, humanitarian organizations need to be prompt, effective, and efficient to support people whose security is threatened. Satellite imagery offers rich and reliable information to support expert decision-making, yet its a...
Accurate damage assessment is critical for reconnaissance and rescue missions. Damage assessment based on unmanned aerial vehicle images is a relevant topic. However, sharing pre- and post-damage knowledge is hindered by the lack of same-viewpoint im...
In the wake of recent strong earthquakes in Croatia, there is a need for a detailed and more comprehensive post-earthquake damage assessment. Given that masonry structures are highly vulnerable to horizontal actions caused by earthquakes and a majori...
Rapid and reliable assessment of structural damage following disasters is critical for prioritizing rescue operations. In this study, we present a unified deep learning framework for building damage assessment from satellite imagery. The proposed app...
This paper presents a multi-level methodology for near real-time seismic damage assessment of multi-story buildings, tailored to the available level of knowledge and information from sensors. The proposed methodology relates changes in the vibratory...
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 i...
A combination of a deterministic approach and fragility analysis is applied to assess tsunami damage caused to buildings. The area selected to validate the model is Imwon Port in Korea. The deterministic approach includes numerical modeling of tsunam...
It is of great significance for emergency rescue to rapidly assess damage of buildings after an earthquake. Some previous methods are time-consuming, data are difficult to obtain, or there is lack of regional damage assessment. We proposed a novel wa...
In this work, the directionality effects during the MW 7.8 earthquake, which occurred in Muisne (Ecuador) on 16 April 2016, were analyzed under two perspectives. The first one deals with the influence of these effects on seismic intensity measures (I...
Rapid and accurate building damage assessment is essential for effective post-disaster response, yet the development of reliable artificial intelligence (AI) models remains constrained by the limited availability of high-quality, operationally releva...
Residential building inspections are periodically required by public authorities. However, current approaches to storing and viewing data concerning an inspection are often collected in reports whose form and limited content hamper the rigorous asses...
Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves...
The main method for assessing the seismic resistance of buildings in the standards of most countries is the linear-spectral method. This method allows for the calculation of the spatial model of a building for seismic load in the elastic range withou...
Building facades are highly exposed envelope components whose degradation affects durability, habitability, urban image, and maintenance planning. Several studies address facade anomalies and service-life prediction. However, a need remains for integ...
This paper presents research concerning the numerical simulation of existing masonry buildings when subjected to pushover analysis. A nonlinear static analysis is undertaken using the commercial software ABAQUS standard, in which masonry structures a...
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...
Remotely sensed data can provide the basis for timely and efficient building damage maps that are of fundamental importance to support the response activities following disaster events. However, the generation of these maps continues to be mainly bas...
The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not...
A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life o...
of 25