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This paper provides a comprehensive examination of post-earthquake structural damage assessment efforts following the Kahramanmaras, Turkey, earthquakes that occurred on February 6, 2023. Drawing on global damage assessment protocols, the study compares and analyzes the methods implemented in the aftermath of the earthquakes, offering insights into lessons learned and challenges faced. The analysis of objections raised regarding the assessment efforts reveals significant changes in structures with moderate and severe damage, emphasizing the need for continuous improvement in assessment strategies. The paper advocates for a realistic and two-stage application method, consideration of crack type and cause, and active involvement of local communities in the assessment process. Furthermore, the study identifies key issues in the current earthquake damage assessment methodology and proposes solutions, including a more precise classification system, regular volunteer training, consideration of secondary disaster risks, and effective communication methods. The paper concludes by underscoring the importance of effective damage assessment in disaster management, addressing objections from the affected population, and continual enhancement of strategies to improve resilience in earthquake-prone regions.

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany. . It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. 109 k m 2 forest area with RGB and NIR bands and 0.2-m spatial resolution. Around 10 7 pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was 92 % . During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management.

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 this study, we aim to assess the performance of Sentinel-1 and Sentinel-2 data for building damage assessment in Kyiv, the capital city of Ukraine, due to the ongoing war with Russia. For damage assessment, we employ a simple and robust SAR log ratio of intensity for the Sentinel-1, and a texture analysis for the Sentinel-2. To suppress changes from other features and landcover types not related to urban areas, we construct a mask of the built-up area using the OpenStreetMap building footprints and World Settlement Footprint (WSF), respectively. As it is difficult to get ground truth data in the ongoing war zone, a qualitative accuracy assessment with the very high-resolution optical images and a quantitative assessment with the United Nations Satellite Center (UNOSAT) damage assessment map was conducted. The results indicated that the damaged buildings are mainly concentrated in the northwestern part of the study area, wherein Irpin, and the neighboring towns of Bucha and Hostomel are located. The detected building damages show a good match with the reference WorldView images. Compared with the damage assessment map by UNOSAT, 58% of the damaged buildings were correctly classified. The results of this study highlight the potential offered by publicly available medium resolution satellite imagery for rapid mapping damage to provide initial reference data immediately after a disaster.

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.

What are the main findings? * Separating building localization from damage classification outperforms multi-class detection on unmanned aerial vehicle (UAV) orthomosaics of post-cyclone Mozambique and provides stronger single-class localization. * Transfer learning remains consistently beneficial despite the domain gap between COCO/ImageNet and UAV disaster imagery, indicating that generic mid-level features transfer well to this task. Separating building localization from damage classification outperforms multi-class detection on unmanned aerial vehicle (UAV) orthomosaics of post-cyclone Mozambique and provides stronger single-class localization. Transfer learning remains consistently beneficial despite the domain gap between COCO/ImageNet and UAV disaster imagery, indicating that generic mid-level features transfer well to this task. What are the implications of the main findings? * A modular two-stage pipeline improves robustness and adaptability for real-world deployments, enabling independent optimization, rapid component swaps, and straightforward extension to new damage taxonomies, geographies, or sensing conditions without full retraining. * By strengthening localization and dedicating classification, the approach better handles challenging, imbalanced classes (e.g., destroyed buildings) and shows improved robustness on non-Western, underrepresented built environments—facilitating more reliable, scalable post-disaster assessment across diverse global contexts. A modular two-stage pipeline improves robustness and adaptability for real-world deployments, enabling independent optimization, rapid component swaps, and straightforward extension to new damage taxonomies, geographies, or sensing conditions without full retraining. By strengthening localization and dedicating classification, the approach better handles challenging, imbalanced classes (e.g., destroyed buildings) and shows improved robustness on non-Western, underrepresented built environments—facilitating more reliable, scalable post-disaster assessment across diverse global contexts. 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 predominantly emphasize Western infrastructure, offering limited representation of African contexts. The EDDA dataset (a Mozambique post-disaster building damage dataset developed under the Efficient Humanitarian Aid Through Intelligent Image Analysis project), addresses this critical gap by capturing rural and urban damage patterns in Mozambique following Cyclone Idai. Despite encouraging early results, significant challenges persist due to task complexity, severe class imbalance, and substantial architectural diversity across regions. Building upon EDDA, this study introduces a two-stage building damage assessment pipeline that decouples localization from classification. We employ lightweight You Only Look Once (YOLO)-based detectors—RTMDet, YOLOv7, and YOLOv8—for building localization, followed by dedicated damage severity classification using state-of-the-art architectures including Compact Convolutional Transformers, EfficientNet, and ResNet. This approach tests whether separating feature extraction tasks—assigning detectors solely to localization and specialized classifiers to damage assessment—yields superior performance compared to multi-class detection models that jointly learn both objectives. Comprehensive evaluation across 640+ model combinations demonstrates that our two-stage pipeline achieves competitive performance (mAP 0.478) with enhanced modularity compared to multi-class detection baselines (mAP 0.455), offering improved robustness across diverse building types and imbalanced damage classes.

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 invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003).

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.

As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization.

Empirical vulnerability models are fundamental tools to assess the impact of future earthquakes on urban settlements and communities. Generally, they consist of sets of fragility curves that are derived from georeferenced post-earthquake damage data. Following the 2015 Nepal earthquake sequence, the World Bank, through the Global Program for Safer Schools, conducted a Structural Integrity and Damage Assessment (SIDA) of about 18,000 school buildings in the earthquake-affected area. In this work, the database is utilized to identify the main structural characteristics of the Nepalese school building stock. For the first time, extended SIDA school damage data is processed to derive fragility curves for the main structural typologies. Data sets for each structural typology are used for a Bayesian updating of existing fragilities to obtain regional models for Nepalese schools. These fragility estimates can be adopted to assess potential seismic losses of the school infrastructure in Nepal. Additionally, they can be used for calibrating loss assessment studies in the wider Himalayan region where the structural typologies are similar.

Disaster damage assessment is an important basis for the objective assessment of the social impacts of disasters and for the planning of recovery and reconstruction. It is also an important research field with regard to disaster mitigation and risk management. Quantitative assessment of physical damage refers to the determination of the physical damage state of the exposed elements in a disaster area, reflecting the aggregate quantities of damages. It plays a key role in the comprehensive damage assessment of major natural hazard-induced disasters. The National Disaster Reduction Center of China has established a technical work flow for the quantitative assessment of disaster physical damage using remote sensing data. This article presents a quantitative assessment index system and method that can be integrated with high-resolution remote sensing data, basic geographical data, and field survey data. Following the 2014 Ludian Earthquake in Yunnan Province, China, this work flow was used to assess the damage to buildings, roads, and agricultural and forest resources, and the assessment results were incorporated into the Disaster Damage Comprehensive Assessment Report of the 2014 Ludian Earthquake for the State Council of China. This article also outlines some possible improvements that can be addressed in future work.