Academic literature on the topic 'Post-event building assessment'
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Journal articles on the topic "Post-event building assessment"
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Rastiveis, H., F. Eslamizade, and E. Hosseini-Zirdoo. "BUILDING DAMAGE ASSESSMENT AFTER EARTHQUAKE USING POST-EVENT LiDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 595–600. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-595-2015.
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After an earthquake, damage assessment plays an important role in leading rescue team to help people and decrease the number of mortality. Damage map is a map that demonstrates collapsed buildings with their degree of damage. With this map, finding destructive buildings can be quickly possible. In this paper, we propose an algorithm for automatic damage map generation after an earthquake using post-event LiDAR Data and pre-event vector map. <br><br> The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of “debris”, “intact building” and “unclassified”. Since textural information make better difference between “debris” and “intact building” classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as “debris” class to the whole building area. Calculating the damage degree for each candidate building, finally, building damage map is generated. <br><br> To evaluate the ability proposed method in generating damage map, a data set from Port-au-Prince, Haiti’s capital after the 2010 Haiti earthquake was used. In this case, after calculating of all buildings in the test area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results were proved the reliability of the proposed method in damage map generation using LiDAR data.
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Liu, Xiaoyu, Shirley J. Dyke, Chul Min Yeum, Ilias Bilionis, Ali Lenjani, and Jongseong Choi. "Automated Indoor Image Localization to Support a Post-Event Building Assessment." Sensors 20, no. 6 (March 13, 2020): 1610. http://dx.doi.org/10.3390/s20061610.
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Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.
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Corbane, Christina, Daniela Carrion, Guido Lemoine, and Marco Broglia. "Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake." Earthquake Spectra 27, no. 1_suppl1 (October 2011): 199–218. http://dx.doi.org/10.1193/1.3630223.
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Following the devastating M7.2 earthquake that affected Haiti on 12 January 2010 two types of building damage assessment maps were produced: 1) area-based damage assessments using pre- and post-event satellite imagery and 2) detailed building-by-building damage assessments using post-event aerial photography. In this paper, we compare the reliability and the usability of area-based damage assessment maps from satellite imagery with respect to the detailed damage assessment from aerial data. The main objective is to better understand how cooperative rapid mapping can steer the more detailed assessments that are typical in determining postdisaster recovery and reconstruction efforts. The results of these experiments indicate that damage assessment maps based on satellite data are capable of capturing the damage pattern, mainly in areas with a high level of damaged and many collapsed structures. However, these maps cannot provide the level of information needed for the quantification of damage intensity.
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Rastiveis, H., F. Samadzadegan, and P. Reinartz. "A fuzzy decision making system for building damage map creation using high resolution satellite imagery." Natural Hazards and Earth System Sciences 13, no. 2 (February 20, 2013): 455–72. http://dx.doi.org/10.5194/nhess-13-455-2013.
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Abstract. Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings.
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May, S., A. Dupuis, A. Lagrange, F. De Vieilleville, and C. Fernandez-Martin. "BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1133–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1133-2022.
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Abstract. Global warming modifies the climate balance. Warming parameters are observed by many Earth Observation satellite systems, and the huge amount of data modifies the way to process them. This paper presents a few studies relative to damage detection on buildings, occurred during natural disasters. Recent advances in deep learning techniques are used for the building detection such as EfficientNet networks. Additional networks as Siamese models are used to evaluate the damage level with pre- and post-event images. Different techniques to merge detection masks are described and compared to a multiclass segmentation network. Results are presented and performances of the different solutions are compared.
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Ge, Pinglan, Hideomi Gokon, and Kimiro Meguro. "Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval." Journal of Disaster Research 14, no. 3 (March 28, 2019): 456–65. http://dx.doi.org/10.20965/jdr.2019.p0456.
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When carrying out change detection for building damage assessment using synthetic aperture radar (SAR) intensity images, it is desirable that the observation conditions of the images are similar and acquisition time is close to the earthquake occurrence time. In this way, the influence of the radar operating system and ground temporal changes can be minimized, facilitating high-accuracy assessment results. However, in practice, especially in poor developing areas, it is difficult to obtain ideal images owing to limited pre-event data archives. In the 2015 Gorkha, Nepal earthquake, the TerraSAR-X satellite captured the influenced Sankhu area before and after the earthquake on May 30, 2010 and May 13, 2015, respectively. The pre-event data was obtained in an ascending path with an incidence angle of 41°, whereas the post-event data was obtained in a descending path with an incidence angle of 33°. To apply the obtained data that had different observation conditions and longtime intervals for building damage assessment, two ways were considered and studied. On one hand, the feasibility of change detection considering these factors was investigated. Pixel statistic characteristics were analyzed in twelve test areas to check the influence of temporal changes, and building footprints were buffered considering two different incidence angles. On the other hand, the reliability of classification based on only post-event data was studied. The results showed good classification performance of some texture parameters, such as the “range value” and “standard deviation,” which are worthy of further study. Moreover, the classification results obtained using the post-event data achieved similar accuracy to that using both the pre- and post-event data, preliminarily indicating the research value of post-event data-based building damage detection as it can solve the pre-event data limitation problem once and for all.
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Naguit, Muriel, Phil Cummins, Mark Edwards, Hadi Ghasemi, Bartolome Bautista, Hyeuk Ryu, and Marcus Haynes. "From Source to Building Fragility: Post-Event Assessment of the 2013 M7.1 Bohol, Philippines, Earthquake." Earthquake Spectra 33, no. 3 (August 2017): 999–1027. http://dx.doi.org/10.1193/0101716eqs173m.
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We use ground-motion simulations of the 2013 Bohol, Philippines, earthquake along with a new post-disaster exposure/damage database to constrain building fragility and vulnerability. The large number of damaged buildings (>70,000) and the wide spread of seismic intensities caused by this earthquake make it an ideal candidate for such a study. An extensive survey was conducted leading to a robust description of over 25,000 damaged and undamaged structures. Ground-motion fields were simulated using ground-motion prediction equations and stochastic modeling, and the estimated and observed values were compared. The finite source model used in the simulation was based on the analysis of aftershocks and SAR data. The ground motions were associated with the empirical database to derive fragility and vulnerability models. Results indicate that the pattern of damage is best captured in the stochastic simulation. Constraints were placed on seismic building fragility and vulnerability models, which can promote more effective implementation of construction regulations and practices.
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Zhang, Ying, Matthew Roffey, and Sylvain G. Leblanc. "A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information." Remote Sensing 13, no. 16 (August 20, 2021): 3297. http://dx.doi.org/10.3390/rs13163297.
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After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection.
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Vetrivel, A., D. Duarte, F. Nex, M. Gerke, N. Kerle, and G. Vosselman. "POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 355–62. http://dx.doi.org/10.5194/isprsannals-iii-3-355-2016.
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Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural damages related to geometrical deformation by combining the complementary information provided by photogrammetric point clouds and oblique images. The developed method detected 87% of damaged elements. The failed detections are due to varying noise levels within the point cloud which hindered the recognition of some structural elements. We observed, in general that the façade regions are very noisy in point clouds. To address this, we propose our second method which aims to detect damages to building façades using the oriented oblique images. The results show that the proposed methodology can effectively differentiate among the three proposed categories: collapsed/highly damaged, lower levels of damage and undamaged buildings, using a computationally light-weight approach. We describe the implementations of the above mentioned methods in detail and present the promising results achieved using multi-temporal oblique imagery over the city of L’Aquila (Italy).
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Vetrivel, A., D. Duarte, F. Nex, M. Gerke, N. Kerle, and G. Vosselman. "POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 355–62. http://dx.doi.org/10.5194/isprs-annals-iii-3-355-2016.
Full textAbstract:
Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural damages related to geometrical deformation by combining the complementary information provided by photogrammetric point clouds and oblique images. The developed method detected 87% of damaged elements. The failed detections are due to varying noise levels within the point cloud which hindered the recognition of some structural elements. We observed, in general that the façade regions are very noisy in point clouds. To address this, we propose our second method which aims to detect damages to building façades using the oriented oblique images. The results show that the proposed methodology can effectively differentiate among the three proposed categories: collapsed/highly damaged, lower levels of damage and undamaged buildings, using a computationally light-weight approach. We describe the implementations of the above mentioned methods in detail and present the promising results achieved using multi-temporal oblique imagery over the city of L’Aquila (Italy).
Dissertations / Theses on the topic "Post-event building assessment"
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Guler, Muhammet Ali. "Detection Of Earthquake Damaged Buildings From Post-event Photographs Using Perceptual Grouping." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12604982/index.pdf.
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Two approaches were developed for detecting earthquake damaged buildings from post-event aerial photographs using shadow analysis and perceptual grouping. In the first approach, it is assumed that the vector boundaries of the buildings are not known a priori. Therefore, only the post-event aerial photographs were used to detect the collapsed buildings. The approach relies on an idea that if a building is fully damaged then, it will not generate a closed contour. First, a median filter is applied to remove the noise. Then, the edge pixels are detected through a Canny edge detector and the line segments are extracted from the output edge image using a raster-to-vector conversion process. After that, the line segments are grouped together using a three-level hierarchical perceptual grouping procedure to form a closed contour. The principles used in perceptual grouping include the proximity, the collinearity, the continuity and the perpendicularity. In the second approach, it is assumed that the vector boundaries of the buildings are known a priori. Therefore, this information is used as additional data source to detect the collapsed buildings. First, the edges are detected from the image through a Canny edge detector. Second, the line segments are extracted using a raster-to-vector conversion process. Then, a two-level hierarchical perceptual grouping procedure is used to group these line segments. The boundaries of the buildings are available and stored in a GIS as vector polygons. Therefore, after applying the perceptual grouping procedure, the damage conditions of the buildings are assessed on a building-by-building basis by measuring the agreement between the detected line segments and the vector boundaries.
Conference papers on the topic "Post-event building assessment"
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Nie, Yuliang, Qiming Zeng, and Jian Jiao. "Building Damage Assessment from Post-Event Polsar Image Based on Opce and Template Matching." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898951.
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Reinartz, P., Jiaojiao Tian, and A. A. Nielsen. "Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs." In 2013 Joint Urban Remote Sensing Event (JURSE). IEEE, 2013. http://dx.doi.org/10.1109/jurse.2013.6550665.
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Furukawa, Keita, and Keita Furukawa. "MODEL SITES EXERCISES FOR ICM IMPLEMENTATION IN JAPAN." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.31519/conferencearticle_5b1b9410f28ed2.51624025.
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The basic act on ocean policy has been enacted since 2007 in Japan, and the basic plan on ocean policy has been endorsed by cabinet originally in 2008, and revised in 2013. The Integrated Coastal Management (ICM) is stated as one of basic measures in the basic act and one of measures the government should take comprehensively in the basic plan. Within the revised basic plan, a clear message of government to “offer assistance to regions” that strive to formulate their own plans (for comprehensively manage land areas and marine zones together) has been discrived. Nevertheless, specific measures by government are not yet implemented in sufficient level. The Ocean Policy Research Institute have set up 5 model sites with collaborative local governments. Since 6 years exercises, ICM implementation processes has been grouped in 5 phases namely, 1) situation understanding, 2) consensus building, 3) ICM planning, 4) adaptive implementation and 5) post assessment process. Variation of phases and necessary assistances will be discrived based on case studies. One of typical example is a collaborative capacity building course with OPRI and Ministry of Land, Infrastructure, Transport and Tourisms (MLIT). It can be an activating event for ICM implementation, and enforcement of local-national network.
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Furukawa, Keita, and Keita Furukawa. "MODEL SITES EXERCISES FOR ICM IMPLEMENTATION IN JAPAN." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.21610/conferencearticle_58b431529a956.
Full textAbstract:
The basic act on ocean policy has been enacted since 2007 in Japan, and the basic plan on ocean policy has been endorsed by cabinet originally in 2008, and revised in 2013. The Integrated Coastal Management (ICM) is stated as one of basic measures in the basic act and one of measures the government should take comprehensively in the basic plan. Within the revised basic plan, a clear message of government to “offer assistance to regions” that strive to formulate their own plans (for comprehensively manage land areas and marine zones together) has been discrived. Nevertheless, specific measures by government are not yet implemented in sufficient level. The Ocean Policy Research Institute have set up 5 model sites with collaborative local governments. Since 6 years exercises, ICM implementation processes has been grouped in 5 phases namely, 1) situation understanding, 2) consensus building, 3) ICM planning, 4) adaptive implementation and 5) post assessment process. Variation of phases and necessary assistances will be discrived based on case studies. One of typical example is a collaborative capacity building course with OPRI and Ministry of Land, Infrastructure, Transport and Tourisms (MLIT). It can be an activating event for ICM implementation, and enforcement of local-national network.