Post-disaster Damage Assessment Research Articles

Rapid post-disaster assessment of residential buildings using Unmanned Aerial Vehicles

Disasters impose significant costs on the built environment due to extensive damages. Accelerated by climate change, disasters such as hurricanes have recently become more frequent and destructive, underscoring the need for rapid post-disaster assessments toward swift search and rescue efforts and damage evaluations. In this regard, this study proposes an image-processing-based method employed on Unmanned Aerial Vehicle (UAV) imagery for automatic assessment of post-disaster damaged buildings. The proposed approach integrates texture-based features, including dissimilarity and homogeneity, along with edge-based features, for which Canny edge detection is employed. The edge-based features introduce novel indices representing the level of irregularity by assessing the entropy as well as the uniformity in the distribution of edge angles. The proposed features are fed into a Naïve Bayesian Classification process to classify damaged and undamaged classes, which makes it possible to account for the underlying uncertainties. The proposed method demonstrates a validation accuracy of 89.3 percent using real-life post-disaster images, effectively distinguishing between damaged and undamaged houses. The findings underscore the potential of employing UAV-captured images and advanced image processing techniques for rapid and accurate post-disaster damage assessment.

Enhancing post-hurricane regional loss assessment with crowdsourcing: A probabilistic inference approach

With the emergence of efficient, widespread social networking infrastructure, along with advances in sensing technology, data retrieval, and data processing techniques, crowdsourcing and participatory sensing have garnered attention as promising approaches for leveraging the general public’s decision-making capacity for more effective and scalable disaster management practices. Although these approaches have recently been acknowledged for their potential in technical and domain-specific tasks, such as post-disaster damage assessment, there is still room for exploration and enhancement. This paper represents a significant stride in this direction by introducing a probabilistic approach designed to improve the reliability and quality of data gathered through participatory disaster damage assessments. By rigorously addressing crucial challenges related to task design and implementing sophisticated truth-inference mechanisms, this study aims to minimize uncertainty and subjectivity in the assessment of building damage states from imagery data conducted by non-expert crowd participants. The proposed methodology is demonstrated using a post-disaster damage database from Hurricane Laura, with Amazon Mechanical Turk (M-Turk) serving as the online crowdsourcing platform. With an achieved accuracy of approximately 50% and a ±1-class accuracy of around 95%, the proposed methodology significantly outperforms other inference methods derived from crowd-assigned labels in similar applications. Furthermore, damage assessment is extended to loss estimation, providing a comprehensive view of end-to-end damage and loss assessment. The findings of this study can significantly contribute to the development of efficient and reliable strategies for post-disaster assessments, ultimately facilitating expedited response and recovery efforts in areas affected by disasters.

A predictive multistage postdisaster damage assessment framework for drone routing

AbstractThis study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real‐life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority‐based metrics.

Dynamic plastic hinge rotation measurement for RC columns under seismic loads using MEMS inclinometers

Plastic hinge rotation is an efficient and essential response parameter for post-disaster damage assessment on RC structures. In this paper, a micro-electronic mechanical system (MEMS) inclinometer-based approach is developed to dynamically measure the plastic hinge rotation of RC columns under earthquake loads. The plastic hinge rotation is regarded as the relative angle of rotation, measured by inclinometers, between cross-sections such that influence from the absolute rotation of floors can be eliminated. Meanwhile, since the inclinometers are densely installed near the bottom, translational acceleration-induced error can be compensated by subtracting the lowest sensor’s output from others’. However, issues caused by rotational and translational vibration are considered as the major challenges in real applications. The contribution of external vibration to the sensor’s overall output is revealed via analysis on a simplified inclinometer model. A numerical study utilizing typical RC column’s deformation characteristics and dimensions is conducted to analyze the influence of major factors: vibrational frequency, sensor position and damage extent. A method for static and dynamic inclinometer calibration is presented. The related calibration experiments are carried out to quantify the sensor’s sensitivity as well as the relationship between rotational frequency and error. The calibration method is validated with a set of vibration tests that use three seismic waves as input. Finally, performance of the proposed approach in full scale structures is investigated through a shake table test.

Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake

Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.

Impacts of droughts and floods on agricultural productivity in New Zealand as measured from space

We estimate the impact of precipitation extremes on the productivity of agricultural land parcels in New Zealand using satellite data. This type of post-disaster damage assessment aims to allow for the quantification of disaster damage when an on-the-ground assessment of damage is too costly or too difficult to conduct. It can also serve as a retroactive data collection tool for disaster loss databases where data collection did not happen at the time. We use satellite-derived observations of terrestrial vegetation (the enhanced vegetation index (EVI)) over the growing season, with data at the land parcel level identifying five land use types (annual and perennial crops, and three types of pasture), and with precipitation records, which we use to identify both excessively dry (drought) and excessively wet (flood) episodes. Using regression analyses, we examine whether these precipitation extremes had an observable impact on agricultural productivity. We find statistically significant declines in agricultural productivity that are associated with both droughts and floods. The average impact of these events is usually less than 1%, but the impacts are quite heterogeneous across years and across regions, with some parcels experiencing a much more significant decline in the EVI. We also identify several impact patterns related to the varying drought and flood vulnerability of the analysed land use types.

Developing a conceptual model of post-disaster damage and loss assessment program in the Iranian health sector: a qualitative study protocol

IntroductionAnnual natural and man-made disasters in Iran eventually lead to casualties and considerable financial loss. The success of a reconstruction programme depends on accurate postdisaster damage and loss assessment. Based...

Case studies of multihazard damage: Investigation of the interaction of Hurricane Maria and the January 2020 earthquake sequence in Puerto Rico

This paper is motivated by the unique findings and observations from reconnaissance visits after the earthquake series in Puerto Rico in January 2020. It aims to discuss the potential interactions of Hurricane Maria and 2020 earthquake series and the considerations they underscore for future field reconnaissance missions. Traditionally, post-disaster damage assessment activities focus on one hazard and overlook the potentially cascading effects of multiple hazards on structures and infrastructure. This paper provides case studies showing the possible interaction of multiple hazards and their cascading effects observed in Puerto Rico. Infrastructure surveyed includes port facilities, buildings (particularly historical structures), and bridge structures. The data collected during the reconnaissance missions reveal how the impacts of Hurricane Maria, along with infrastructure aging and delayed repair and recovery activities, may have influenced the damage level and failure modes observed during the earthquake sequence a few years after. These case studies illustrate the nature of multihazard interactions and how these effects should be documented during post-disaster assessments. Beyond the insights gained from the case studies illustrated in this paper, the field survey instrument is provided as a basis for future reconnaissance studies, and the full set of reconnaissance data collected are published on the NSF funded NHERI DesignSafe cyberinfrastructure. As a result, this work not only provides data from Puerto Rico that can inform future damage and recovery modeling efforts, but also offers survey instruments and a field data collection process that is particularly tailored to cases where multihazard effects are at play.

Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the flood-affected area, promoting post-disaster farmland restoration, and providing an auxiliary decision-making basis for flood prevention and disaster relief departments. Taking the flood event in Henan and Shanxi Provinces as example, based on the characteristics of the variations in radar data and optical data before and after the disaster, we propose an extent information extraction method for the flood inundation area and the flood-affected area of agricultural land. This method consists of change detection, threshold extraction, and superposition analysis, which weakens the negative impact of the radar data speckle noise and cloud contamination of the optical data on the extraction of the agricultural land flooding to a certain extent. The method was developed based on a flood event in Henan Province and validated in Shanxi Province. The results show that the production of this method have a clear boundary and accurate extent, and the overall precisions of the flood inundation area and flood-affected area extraction are 0.87 and 0.92, respectively. The proposed method combines the advantages of both radar and optical remote sensing data in extracting the specific extents of the flood inundation area and the flood-affected area in large spatial scale. Finally, the impact of time window size to the performance of the method is further analyzed. In the application of the proposed method, the Google Earth Engine (GEE) platform provides a low-cost, fast, and convenient way to extract flood information from remote sensing data. The proposed scheme provides a scientific data basis for restoring production of agricultural land after a flood disaster, as well as for national post-disaster damage assessment and disaster relief decision making.

SAM-VQA: Supervised Attention-Based Visual Question Answering Model for Post-Disaster Damage Assessment on Remote Sensing Imagery

Each natural disaster leaves a trail of destruction and damage that must be effectively managed to reduce its negative impact on human life. Any delay in making proper decisions at the post-disaster managerial level can increase human suffering and waste resources. Proper managerial decisions after any natural disaster rely on an appropriate assessment of damages using data-driven approaches, which are needed to be efficient, fast, and interactive. The goal of this study is to incorporate a deep interactive data-driven framework for proper damage assessment to speed up the response and recovery phases after a natural disaster. Hence, this paper focuses on introducing and implementing the Visual Question Answering (VQA) framework for post-disaster damage assessment based on drone imagery, namely Supervised Attention-Based VQA (SAM-VQA). In visual question answering, query-based answers from images regarding the situation in disaster-affected areas can provide valuable information for decision-making. Unlike other computer vision tasks, visual question answering is more interactive and allows one to get instant and effective scene information by asking questions in natural language from images. In this work, we present a VQA dataset and propose a novel supervised attention-based VQA framework (SAM-VQA) for post-disaster damage assessment on remote sensing images. Our model outperforms state-of-the-art attention-based VQA techniques, including Stacked Attention Networks (SAN) [1] and Multi-modal Factorized Bilinear (MFB) with Co-Attention [2]. Furthermore, our proposed model can derive appropriate visual attention based on questions to predict answers, making our approach trustworthy.

Data Collection Tools for Post-Disaster Damage Assessment of Building and Lifeline Infrastructure Systems

Data Collection Tools for Post-Disaster Damage Assessment of Building and Lifeline Infrastructure Systems

An uncertainty-aware framework for reliable disaster damage assessment via crowdsourcing

Accurate and timely estimation of incurred damages is a critical component of effective disaster management, usually performed by trained inspectors and experts. The limitations in resources and workforce can hinder the timely acquisition of critical information and make the process costly. Crowdsourcing and participatory disaster damage assessment have emerged as a possible solution to address this challenge. However, such approaches generally suffer from a lack of reliability. This research improves the effectiveness of crowdsourcing in post-disaster damage assessment by enhancing the content and reliability of information gathered through public participation. The paper presents a novel framework for quantification and reduction of uncertainty in the outcome of participatory damage assessment. First, to reduce the complexity and subjectivity, the classification of overall damage state is decomposed into more straightforward microtasks in the form of a questionnaire survey. A decision rule is implemented to infer the damage state of buildings from the participant responses. Second, an information-theoretic model based on a maximum a posteriori probability estimation is presented for obtaining an accurate probabilistic description of the inferred damage states while quantifying and accounting for the reliability of the citizen participants as well as the relative ambiguity of images. A pilot study is presented by involving 70 non-expert citizen participants to assess the post-disaster imagery of 60 buildings collected following Hurricane Harvey. A comparison of the outcome with the available expert labels shows relatively high accuracy. The proposed model also outperforms the common majority-vote approach, especially as the number of unreliable participants increases.

Residual Mechanical Properties and Constitutive Model of High-Strength Seismic Steel Bars through Different Cooling Rates.

In this study, the high-temperature test (i.e., temperature to 1000 °C) is conducted on 600 MPa seismic steel bars, and its residual mechanical properties and constitutive relations are investigated though three cooling rates, i.e., under air, furnace, and water-cooling conditions. Results show that three cooling methods have significant effects on the apparent characteristics of 600 MPa steel bars, when the heating temperature is greater than 600 °C. In addition, the ultimate and yield strength of steel bars have been significantly affected by different cooling methods, with increasing heating temperature. However, the elastic modulus is significantly not affected by temperature. Furthermore, the elongation rate after fracture and the total elongation rate at the maximum force do not change significantly, when the heating temperature is less than 650 °C. The elongation rate, after fracture, and the total elongation rate, at the maximum force, have different changes for three cooling methods. The degeneration of the stress–strain curves occurs when the heating temperature is high. The two-fold line, three-fold line, and Ramberg–Osgood models are developed based on the stress–strain curve characteristics of steel bars after cooling. The fire resistance of 600 MPa steel bars of reinforced concrete structure is analyzed, which provides a basis for post-disaster damage assessment, repair, and reinforcement of the building structure.

Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

This paper presents the design and construction and control of a quadcopter drone for Aerial Data Collection (ADC). The frame of the drone was designed using CadDian Software and the parts were printed using a 3D printer. The flight controller was based on Arduino board using an Atmega328p microprocessor with GSM, GPS and GPRS for sending data over the internet and also enhancing long range flight. A feedback control system was developed and tested to control the stability of drone. The proposed control strategy of the drone was tested for a case of pursuit of trajectory and also for speed of response and the findings were very positive confirming the appropriateness of the control measures for independent and autonomous flying with promising precision. This Unmanned Aerial Vehicle (UAV) fitted with IoT has the capability of collecting and sending data over the internet and therefore can be used in many applications including risk assessment, forestry management, urban planning, coastal zone management, infrastructure monitoring, post-disaster damage assessment and delivery of medical supplies.

Assessment of Building Vulnerability Curve Subjected to Debris-Flow

In this study, the vulnerability curves for masonry and concrete frame buildings are assessed based on building fragility curves for the debris-flow caused by landslides on mountain slopes. The First-Order Second-Moment (FOSM) method is used to estimate the building fragility curve (expressed as probability of damage exceedance) subjected to debris-flow. In this method, the horizontal displacement of a building impacted by debris-flow and the statistics of resistance (i.e., building displacement) following four different damage states (i.e., slight, moderate, extensive, and complete) are utilized to estimate the building fragility curve. The building vulnerability curves (expressed as mean probability of damage) were evaluated based on the estimated building fragility curve and corresponding mean damage ratio for each damage state and were verified by calculating the root mean square error with datasets obtained from post-disaster damage assessment. In this study, the effects of structural material, type, and height on the building vulnerability curves were also studied. All vulnerability curves of buildings estimated in this study were fitted and databased using parameters of the log-normal cumulative distribution function and can be used to measure the performance of buildings in debris-flow prone areas as well as to provide information for risk and loss assessment.

Methodology and Validation of UAV-Based Video Analysis Approach for Tracking Earthquake-Induced Building Displacements

AbstractUnmanned aerial vehicle (UAV) imagery has recently emerged as a promising alternative for operational condition inspection and postdisaster damage assessment of civil structures (e.g., brid...

Mud masonry houses in Nepal: A detailed study based on entire reconstruction scenario in 31 earthquake-affected districts

The 7.8Mw 2015 Gorkha earthquake and its aftershocks caused a massive loss of human-lives and structures. But even after 5 years of this devastating earthquake, the inability of the Government of Nepal (GoN) to complete the reconstruction works in 31 earthquake-affected regions has emerged several queries about its present status, achievements, and failures with possible solutions. So, realizing the lack of detailed studies done in this sector and also the immediate need for visualizing the entire reconstruction scenario to contribute timely and quality reconstruction, this paper is written. A detailed 7 years study (since early 2012) in the form of the reconnaissance survey (visiting earthquake-affected regions, both before and after the Gorkha earthquake), active group discussions and questionnaires with engineers, local people, and the masons were conducted. Officially working in a post-disaster damage assessment and reconstruction project under GoN was a plus point for this study to further obtain the detailed information. The overall findings were focused on entire construction practices (including common construction and structural deficiencies) adopted in new mud masonry houses and their comparison with old and finally relate this information to express the issues and challenges faced in reconstruction phases. Closer study shows that although the majority (>80%) of newly constructed mud masonry houses were comparatively better than old, but the regional variation in reconstruction rate and overall delay in reconstruction (>30% houses remaining) and retrofitting activities (>99% remaining) has generated further risk in people’s life. Around 15%–20% earthquake-victim has repaired their old cracked houses or construct new homes without any seismic consideration. Along with this, the current housing design (≈10% having single-room and >70% smaller double-rooms with low height attic space) has clearly shown the inability to fulfil the space requirements of local people. Presenting the obtained findings through the data and further verifying each section using good quality pictures as evidence has been the highlighting part of this paper and also played a vital role in establishing clarity for readers. Based on the overall reconstruction scenario (especially focused on deficiencies in mud masonry houses), this paper recommends possible things to be targeted for its better upliftment.

Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets

This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a network from scratch for each image to be segmented. We propose a convolutional encoder–decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers’ statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image boosts the network performance with minimal human intervention. We evaluate our architecture over three data sets of satellite images, showing the state-of-the-art performance in binary segmentation of previously unseen images and competitive performance with respect to more complex techniques in a multiclass segmentation task.

Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data

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.

Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmentation as well as to alternative fusion approaches for the segmentation of flooded buildings and find that our model performs best on both tasks. We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely available very high-resolution data. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.