Damage Mapping Research Articles

Rasmussen's Aneurysm: A Rare Cause of Massive Hemoptysis in Pulmonary Tuberculosis

Rasmussen's aneurysm is a rare cause of hemoptysis in patients with tuberculosis. It is an under-recognised vascular complication of active pulmonary tuberculosis, with a mortality rate of over 50%. It is secondary to an aneurysmal dilatation of the pulmonary artery adjacent to tuberculous cavity. Thoracic CT angiography is the gold standard for making a positive diagnosis and precising the hemorrhagic site, with a mapping of the pulmonary artery damage. Rupture of the artery can cause massive hemoptysis, which can sometimes be devastating. It is important to remember that hemoptysis persists despite successful bronchial artery embolisation in all patients with tuberculosis.

A Unified Virtual Model for Real-Time Visualization and Diagnosis in Architectural Heritage Conservation

The aim of this paper is to propose a workflow for the real-time visualization of virtual environments that supports diagnostic tasks in heritage buildings. The approach integrates data from terrestrial laser scanning (3D point clouds and meshes), along with panoramic and thermal images, into a unified virtual model. Additionally, the methodology incorporates several post-processing stages designed to enhance the user experience in visualizing both the building and its associated damage. The methodology was tested on the Medieval Templar Church of Vera Cruz in Segovia, utilizing a combination of visible and infrared data, along with manually prepared damage maps. The project results demonstrate that the use of a hybrid digital model—combining 3D point clouds, polygonal meshes, and panoramic images—is highly effective for real-time rendering, providing detailed visualization while maintaining adaptability for mobile devices with limited computational power.

Global screening of base excision repair in nucleosome core particles

DNA damage is a fundamental molecular cause of genomic instability. Base excision repair (BER) is one line of defense to minimize the potential mutagenicity and/or toxicity derived from damaged nucleobase lesions. However, BER in the context of chromatin, in which eukaryotic genomic DNA is compacted through a hierarchy of DNA-histone protein interactions, is not fully understood. Here, we investigate the activity of BER enzymes at 27 unique geometric locations in a nucleosome core particle (NCP), which is the minimal unit of packaging in chromatin. The BER enzymes include uracil DNA glycosylase (UDG), AP endonuclease 1 (APE1), DNA polymerase β (Pol β), and DNA ligase IIIα complexed with X-ray repair cross complementing group 1 (LigIIIα/XRCC1). This global analysis of BER reveals that initiation of the repair event by UDG is dictated by the rotational position of the lesion. APE1 has robust activity at locations where repair is initiated whereas the repair event stalls at the Pol β nucleotide incorporation step within the central ∼45 bp of nucleosomal DNA. The final step of the repair, catalyzed by LigIIIα/XRCC1, is achieved only in the entry/exit regions of the NCP when nick sites are transiently exposed by unwrapping from the histones. Kinetic assays further elucidate that the location of the damaged lesion modulates enzymatic activity. Notably, these data indicate that some of the BER enzymes can act at a significant number of locations even in the absence of chromatin remodelers or other cellular factors. These results inform genome wide maps of DNA damage and mutations and contribute to our understanding of mutational hotspots and signatures.

Unsupervised Bushfire Burn Severity Mapping Using Aerial and Satellite Imagery

Abstract. It is critical to assess bushfire impact rapidly and accurately because bushfires play a significant role in forest degradation and present a threat to ecosystems and human lives. Over the past decades, several supervised algorithms of burn severity mapping have been proposed, facing the significant drawback of time-consuming labeling. Moreover, there is no robust framework for burn severity mapping through fusing multi-sensor, multi-resolution, and multi-temporal remote sensing imagery from satellite and aerial platforms. Therefore, this paper presents an unsupervised two-step pipeline: processing 2D data followed by 3D data for burn severity mapping, both of which are acquired from either aircraft or satellites. For the 2D data processing, our proposed unsupervised burned area detection (UsBA detection) model enhances burned area mapping accuracy by integrating Ultra-High Resolution (UHR) aerial imagery with bi-temporal medium-resolution PlanetScope imagery, using a Segment Anything Model (SAM)-assisted UNetFormer (pre-trained on the target-style public dataset – LoveDA Rural) for refinement. The model demonstrates superior burned area segmentation, evidenced by improved evaluation metrics calculated from labeled test sites. For the 3D analysis, the burned areas extracted from 2D processing are further assessed using pre- and post-event airborne laser data. We implement a voxel-based workflow, including necessary steps such as ground filtering through Superpoints in RANSAC Planes (SiRP) method and biomass change analysis. The results indicate that the 3D branch provides a reliable lower bound of the actual damage map, because the vegetation growth between two measurements remains, in essence, undetected. The proposed framework offers a more accurate and robust solution for burn severity mapping utilizing combined 2D and 3D data, evaluated on a multi-source dataset from a real bushfire event that occurred in Bushland Park, South Australia.

650P Development and implementation of enhanced AI-derived DTI features for precision mapping of neural tract damage in myotonic dystrophy

650P Development and implementation of enhanced AI-derived DTI features for precision mapping of neural tract damage in myotonic dystrophy

Broadband nonlinear delay and sum (NL-DAS) for baseline-free damage imaging using a sparse sensor network

The Delay and Sum (DAS) method is a well-known damage imaging technique in sparse array guided wave imaging. However, the DAS method requires prior knowledge on the stable baseline state, which makes it ineffective under different operational or environmental conditions, e.g. temperature and moisture, and altered experimental settings, e.g. degraded bonding quality of sensors.This paper proposes a Nonlinear Delay and Sum (NL-DAS) method which does not require prior knowledge about the baseline state, but instead exploits the presence of nonlinear wave/defect interactions. Relatively high-power broadband sweep sine signals are supplied to a sparse sensor array to activate a multitude of nonlinear wave/defect interactions. Application of time–frequency filtering to the response signals allows the isolation of damage-induced broadband nonlinear responses. The isolated broadband nonlinear response signals are subsequently decomposed into a series of narrowband tone burst responses from which a set of damage maps are constructed. To improve the quality of the constructed damage maps, an automated framework is proposed to obtain the frequency-dependent directional group velocities without requiring prior knowledge on material properties. Finally, the resulting set of damage maps is fused into a single broadband NL-DAS damage map.The proposed broadband NL-DAS approach is demonstrated on a simulation dataset, generated with 3D Finite Element method, which is representative for a cross-ply carbon fiber reinforced polymer (CFRP) containing a delamination defect. The damage imaging performance is studied for different test conditions in terms of (i) signal-to-noise ratio, (ii) number of sensors and (iii) excitation bandwidth. Experimental validation is illustrated on a CFRP plate containing a barely visible impact damage, and on a CFRP A320 component with a disbond at one of the stiffeners.

On the Use of a Chloride or Fluoride Salt Fuel System in Advanced Molten Salt Reactors, Part 3; Radiation Damage

Structural materials in fast reactors with harsh radiation environments due to high energy neutrons—compared to thermal reactors—potentially suffer from a higher degree of radiation damage. This radiation damage can change the thermophysical and mechanical properties of materials and, as a result, alter their performance and effective lifetime, in some cases leading to their disintegration. These phenomena can jeopardize the safety of fast reactors and thus need to be investigated. In this study, the effect of radiation damage on the vessels of molten salt fast reactors (MSFR) was evaluated based on two fundamental radiation damage parameters: displacement per atom (dpa) and primary knock-on atom (pka). Following the previous part of this article (Parts 1 and 2), an iMAGINE reactor core design (University of Liverpool, UK—chloride-based salt fuel system) and an EVOL reactor core design (CNRS, Grenoble, France, fluoride-based salt fuel system) with stainless steel and nickel-based alloy material vessels, respectively, were considered as case studies. The SPECTER and SPECTRA-PKA codes and a PTRAC card of MCNPX, integrated with a module which has been developed in MATLAB, named PTRIM and SRIM-2013 (using binary collision approximation), were employed individually to calculate and compare dpa and PKA (this master module containing all three tools has been appended to the iMAGINE-3BIC package for future use during reactor operations). Additionally, SRIM-2013 was applied in a 3D simulation of a radiation damage map on a small sample of vessels based on the calculated PKA. Our results showed a higher degree of radiation damage in the iMAGINE vessel compared to the EVOL one, which could be expected due to the harder neutron flux spectrum of the iMAGINE core compared to EVOL. In addition, the nickel alloy vessel showed better radiation damage resistance against high energy neutrons compared to the stainless steel one, although more investigations are required on thermal neutrons and alloy corrosion mechanisms to determine the best material for use in MSFR vessels.

Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.

Assessing the impact of rising ambient temperatures on urban mortality rates using satellite mapping

Introduction. In the context of the change in global climate, the gain in the mortality rate due to an elevation of the temperature in the urban environment with the appearance of “heat waves” becomes an urgent problem. Materials and methods. To analyze the spatial distribution of risks and damages from premature mortality, there were selected cities located in several climatic zones of Russia including Omsk, Astrakhan, Rostov-on-Don, Lipetsk, Arkhangelsk. For satellite mapping of risks and damages from premature deaths caused by an elevation of the temperature in urban spaces, materials from summer surveys by Landsat series satellites were used. Results. The analysis of all satellite maps allows concluding the risks of an increase in urban temperature, potential deaths, and economic damage within the territories of the studied cities to be unevenly distributed. Advanced values of potential mortality are localized in places where high population density coincides with an enhanced risk of the temperature rise. The highest damage values were noted in Rostov-on-Don and Astrakhan, the lowest – in Arkhangelsk. Limitations. Mortality analysis was carried out without taking into account the age and sex structure of the population. Conclusion. The results of applying the methodology for satellite mapping of risks and economic losses from premature mortality due to rising urban temperatures showed accuracy acceptable for making management decisions to protect the health of the urban population. The results of satellite mapping of these events can be used in the development of preventive measures aimed at reducing premature mortality.

Mapping building facades using panoramic images captured by Unmanned Aerial Vehicle (UAV)

This work aims to verify the building pathologies of DTIC’s building front (Diretoria de Tecnologia da Informação e Comunicação at Federal University of Pampa – Alegrete/RS, Brazil) by using an Unmanned Aerial Vehicle (UAV). The study was conducted before and six months after its maintenance. Images and inspection records were used to elaborate the Damage Maps and the Grid Method for pathologies quantifications. The comparison of the Damage Maps before and after maintenance has revealed that the intervention (paint) was not efficient, since it has disguised temporarily the anomalies without correcting them. The use of UAV proved to be a good tool for building inspections, providing effective diagnosis, time-saving and security for the operator without working at height.

Identificação de manifestações patológicas através da elaboração de mapas de danos de fachadas de um prédio histórico da cidade do Recife, PE: Estudo de caso do Bloco A da Escola Politécnica de Pernambuco

The city of Recife has numerous historic buildings that have existed for over 100 years and are part of the rich culture of this region. Over the years, these buildings get damaged due to the action of external weather and, in some cases, human action, which ends up generating numerous pathological manifestations that require correction. Therefore, this research's main objective is to study and identify the pathological manifestations present on the facades of block A of the Polytechnic School of the University of Pernambuco, aiming to assist in future, more sustainable restoration works in this building. As a methodological basis, a photographic inspection of the study object was carried out, followed by the identification of the pathological manifestations present on each facade, analysis of the photos and the development of damage maps using AutoCAD software. As a result, several pathological manifestations were found on the four facades of the building and from the preparation of damage maps it was possible to identify the areas that needed the most urgent repairs, due to the high degree of incidence of anomalies. The results demonstrated the importance of conserving historical heritage, since these buildings are living memories of a people's culture, in addition to being important instruments for the economic, social and environmental development of cities.

Data Collection for Cultural Heritage Risk Management: the Damage Map through Heritage Building Information Modeling (HBIM) Project Applied to the Façade of St Francis of Assisi, Ouro Preto, Brazil

ABSTRACT The development of precise analytical methodologies, together with the systematic use of verifiable data, aims to raise standards for treatment, risk management, and maintenance programs. This endeavor aims to diminish the frequency of subsequent restoration cycles, thereby positively impacting public spending and fostering the preservation of the authentic characteristics of historical buildings, particularly by mitigating the effects of decay. The Damage Map through Heritage Building Information Modeling (HBIM) project, funded by the National Council for Scientific and Technological Development (CNPq) and Minas Gerais Research Support Foundation (FAPEMIG), both Brazil, aims to establish protocols for risk management in historical buildings, with a focus on decorative stone elements integrated into the construction. For this reason, the stone relief on the façade of the church of St Francis of Assisi in Ouro Preto, Brazil, was chosen as a case study. This paper outlines the data collection methodology applied to study the stone. This approach encompasses historical examinations of restoration cycles, along with preliminary findings derived from extensive fieldwork, conducted by a multidisciplinary team in 2023. It elucidates the data collection stage, employing photography and aerial photogrammetry, ultraviolet fluorescence photography, mobile laser scanning, and thermography. Subsequently, all collected data will be used to construct a diagnostic model within the HBIM.

Typhoon-Induced Forest Damage Mapping in the Philippines Using Landsat and PlanetScope Images

Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to the intensity and impact of disasters. However, little attention has been paid to forest cover losses due to typhoons during disaster assessments. In this study, forest damage caused by typhoons was measured using harmonic analysis of time series (HANTS) with Landsat-8 Operation Land Imager (OLI) images. The ΔHarmonic Vegetation Index was computed by calculating the difference between HANTS and the actual observed vegetation index value. This was used to identify damaged areas in the forest regions and create a damage map. To validate the reliability of the results, the resulting maps produced using ΔHarmonic VI were compared with the damage mapped from PlanetScope’s high-resolution pre- and post-typhoon images. The method achieved an overall accuracy of 69.20%. The accuracy of the results was comparable to the traditional remote sensing techniques used in forest damage assessment, such as ΔVI and land cover change detection. To further the understanding of the relationship between forest and typhoon occurrence, the presence of time lag in the observations was investigated. Additionally, different contributing factors in forest damage were identified. Most of the forest damage observed was in forest areas with slopes facing the typhoon direction and in vulnerable areas such as near the coast and hill tops. This study will help the government and forest management sectors preserve forests, which will ultimately result in the development of a more resilient community, by making it easier to identify forest areas that are vulnerable to typhoon damage.

Cultural Heritage Preservation: Challenges and Prospects for Preserving Art and Religious Traditions in Indonesia

This research presents an alarming picture of the destruction of religious arts and traditions in Indonesia. The damage is caused not only by the physical ravages of time due to pollution and environmental change but also by human actions such as vandalism in places of worship. This study aims to identify the challenges faced in preserving religious cultural heritage and explore prospects for its sustainable maintenance. Data collection was conducted through observation and analysis of relevant documents. The research results reveal a damage map that illustrates the alarming severity of both natural and social processes. The conclusions highlight the need for concrete action to safeguard Indonesia's religious arts and traditions from being lost to time, as well as to strengthen awareness of the importance of preserving this precious cultural heritage.

Development of mechanics-based fragility curves for the Italian masonry school asset

This article presents the derivation of a fragility model for the Italian masonry school building asset, comprising 265 sets of fragility curves for as many building types, classified on the basis of few parameters: construction age, number of stories, plan area, and type of masonry (i.e. with regular or irregular pattern). The fragility assessment was carried out by means of parametric analyses, generating more than 7500 samples which were then analyzed through the mechanics-based procedure Vulnus. Sample fragilities were then linearly combined to obtain fragility curves consistent with the adopted taxonomy based on few parameters. A macroseismic–heuristic model from the literature was used to extend the fragility model to five damage states, according to the European Macroseismic Scale (EMS98). The proposed model was compared to empirical information in terms of observed damage on three existing schools and fragility curves recently derived by processing data of school damaged by the 2009 L’Aquila earthquake, showing a satisfactory agreement. In addition, a comparison with fragility sets for residential buildings was carried out. Both fragility models were developed with the same procedure, so as to point out differences between schools and ordinary buildings. Similar fragilities were observed for schools and residential buildings built before 1945, whereas for later periods, schools showed a higher fragility than the residential asset. Finally, seismic damage maps were developed at national scale showing the distribution of expected damage as a possible application of the derived model.

Flood Risk Assessment of the Middle Reach of the Helmand River, Afghanistan

The middle reach of the Helmand River is prone to floods, and flood hazard analysis is necessary to manage the flood risk. As the study site is a less-developed basin where available geographical and hydrological data are limited, we must utilize available limited data to conduct the flood hazard analysis. A frequency analysis of the annual peak flow rate was performed using available hydrological data for making representative flood hydrographs. A land-use map is derived by classifying recent Landsat images. Digital elevation model derived by Advanced Land Observing Satellite, roughness coefficient correlated with the land-use map, and the representative flood hydrographs are fed into hydraulic model HEC-RAS 2D. Flood inundation area, depth, and velocity for different flood return periods are estimated. Based on local hazard intensity and vulnerability, risk maps for evacuation and vulnerability of crops on the floodplain are obtained. Spatial flood risk characteristics differ at the upstream and downstream reaches because of distinct floodplain topography. Flood countermeasures for each reach are proposed based on the spatial characteristics of the flood risk. To provide risk information for residents within the region, the flood hazard, risk, and damage maps derived in this study are published as a web map application and a mobile native application.

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

AbstractEfficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports.

Remote sensing‐based mapping of structural building damage in the Ahr valley

AbstractFlood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life‐threatening condition of a building, which provide crucial information for disaster response and recovery. Since traditional on‐site data collection shortly after a disaster is challenging, remote sensing data can be of great help, cover a wider area and be deployed earlier in time than on‐site surveys. However, this has its challenges and limitations. We elucidate on that by presenting two case studies from flash floods in Germany. First, we assessed the reliability of an existing flood damage schema, which differentiates from minor (structural) damage to complete building collapse. We compared two on‐site raters of the 2016 Braunsbach flood, reaching an excellent level of reliability. Second, we mapped structural building damage after the flood in the Ahr valley in 2021 using a textured 3D mesh and orthophotos. Here, we evaluated the remote sense‐based damage mapping done by three raters. Although the heterogeneity of ratings using remote sensing data is larger than among on‐site ratings, we consider it fit‐for‐purpose when compared with on‐site mapping, especially for event documentation and as basis for financial damage estimation and less complex numerical modelling.

Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping

The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system that ingests data from multiple non-traditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through geographic regression analysis using official ground-truth data, showcasing its strong performance and explanatory power. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.

Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scanning

Expert-dependent visual inspection of vertical-type tunnels is often not only dangerous for workers, but also unreliable of the inspection results. To address the technical issues, a deep learning-based 3D digital damage mapping technique using an unmanned fusion data-scanning system is described in this article. The proposed system acquires raw digital images and point cloud data by moving vertically within the tunnel. Subsequently, optimal images are automatically selected and used for deep learning-based damage evaluation and fixed camera pose-based 3D digital modeling. Finally, a 3D digital damage map is generated by mapping the damage features onto the 3D digital map based on the predetermined camera pose. The proposed technique was experimentally validated in two different in-situ testbeds in South Korea, revealing that averaged precision and recall values are 90.89% and 98.18%, respectively. It is expected that the proposed technique can improve in-situ workability as well as data reliability.