Journal articles on the topic 'Post-event building assessment'
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1
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.
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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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YAMAZAKI, FUMIO, and MASASHI MATSUOKA. "REMOTE SENSING TECHNOLOGIES IN POST-DISASTER DAMAGE ASSESSMENT." Journal of Earthquake and Tsunami 01, no. 03 (September 2007): 193–210. http://dx.doi.org/10.1142/s1793431107000122.
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This paper highlights the recent applications of remote sensing technologies in post-disaster damage assessment, especially in the 2004 Indian Ocean tsunami and the 2006 Central Java earthquake. After the 2004 Indian Ocean tsunami, satellite images which captured the affected areas before and after the event were fully employed in field investigations and in tsunami damage mapping. Since the affected areas are vast, moderate resolution satellite images were quite effective in change detection due to the tsunami. Using high-resolution optical satellite images acquired before and after the 2006 Central Java earthquake, the areas of building damage were extracted based on pixel-based and object-based land cover classifications and their accuracy was compared with visual inspection results. In the Central Java earthquake, ALOS/PALSAR captured a SAR image of the affected area one day after the event as well as pre-event times. Taking the difference of the pre-event correlation and the pre-and-post event correlation, the areas affected by the earthquake were also identified. From these examples, the use of proper satellite imagery is suggested considering the area to cover, sensor type, spatial resolution, satellite's retake time etc., in post-disaster damage assessment.
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Malek, Amirmasoud, Allan Scott, Stefano Pampanin, and Gregory MacRae. "Post-event damage assessment of concrete using the fluorescent microscopy technique." Cement and Concrete Research 102 (December 2017): 203–11. http://dx.doi.org/10.1016/j.cemconres.2017.09.015.
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Miura, Hiroyuki, Saburoh Midorikawa, and Masashi Matsuoka. "Building Damage Assessment Using High-Resolution Satellite SAR Images of the 2010 Haiti Earthquake." Earthquake Spectra 32, no. 1 (February 2016): 591–610. http://dx.doi.org/10.1193/033014eqs042m.
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Damage to individual buildings in an urban area of Port-au-Prince, Haiti, from the 2010 Haiti earthquake was assessed by means of high-resolution synthetic aperture radar (SAR) intensity images and ancillary building footprints. A comparison of pre- and post-event images and a building damage inventory showed that backscattering intensity between images was more significantly changed in collapsed buildings than in less damaged buildings. The linear discriminant function, based on the difference and correlation coefficient of the images was developed to detect collapsed buildings. The result showed that almost 75% of the buildings were correctly detected by discriminant analysis. An accuracy assessment revealed the difficulty of detecting small and congested buildings because the number of image pixels was too small and the buildings were obscured by neighboring buildings and other features in the images.
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Marshall, Justin D., Kishor Jaiswal, Nathan Gould, Fred Turner, Bret Lizundia, and Jim C. Barnes. "Post-Earthquake Building Safety Inspection: Lessons from the Canterbury, New Zealand, Earthquakes." Earthquake Spectra 29, no. 3 (August 2013): 1091–107. http://dx.doi.org/10.1193/1.4000151.
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The authors discuss some of the unique aspects and lessons of the New Zealand post-earthquake building safety inspection program that was implemented following the Canterbury earthquake sequence of 2010–2011. The post-event safety assessment program was one of the largest and longest programs undertaken in recent times anywhere in the world. The effort engaged hundreds of engineering professionals throughout the country, and also sought expertise from outside, to perform post-earthquake structural safety inspections of more than 100,000 buildings in the city of Christchurch and the surrounding suburbs. While the building safety inspection procedure implemented was analogous to the ATC 20 program in the United States, many modifications were proposed and implemented in order to assess the large number of buildings that were subjected to strong and variable shaking during a period of two years. This note discusses some of the key aspects of the post-earthquake building safety inspection program and summarizes important lessons that can improve future earthquake response.
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Kim, W., N. Kerle, and M. Gerke. "Mobile Augmented Reality in support of building damage and safety assessment." Natural Hazards and Earth System Sciences Discussions 3, no. 4 (April 16, 2015): 2599–627. http://dx.doi.org/10.5194/nhessd-3-2599-2015.
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Abstract. Rapid and accurate assessment of the state of buildings in the aftermath of a disaster event is critical for an effective and timely response. For rapid damage assessment of buildings, the utility of remote sensing (RS) technology has been widely researched, with focus on a range of platforms and sensors. However, RS-based approach still have limitations to assess structural integrity and the specific damage status of individual buildings. Consequently, ground-based assessment conducted by structural engineers and first responders is still required. This paper demonstrates the concept of mobile Augmented Reality (mAR) to improve performance of building damage and safety assessment in situ. Mobile AR provides a means to superimpose various types of reference or pre-disaster information (virtual data) on actual post-disaster building data (real building). To adopt mobile AR, this study defines a conceptual framework based on Level of Complexity (LOC). The framework consists of four LOCs, and for each of these the data types, required processing steps, AR implementation, and use for damage assessment, are described. Based on this conceptualization we demonstrate prototypes of mAR for both indoor and outdoor purposes. Finally, we conduct a user evaluation of the prototypes to validate the mAR approach for building damage and safety assessment.
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Shao, Jinyuan, Lina Tang, Ming Liu, Guofan Shao, Lang Sun, and Quanyi Qiu. "BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery." Remote Sensing 12, no. 10 (May 22, 2020): 1670. http://dx.doi.org/10.3390/rs12101670.
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The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.
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Kim, W., N. Kerle, and M. Gerke. "Mobile augmented reality in support of building damage and safety assessment." Natural Hazards and Earth System Sciences 16, no. 1 (February 1, 2016): 287–98. http://dx.doi.org/10.5194/nhess-16-287-2016.
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Abstract. Rapid and accurate assessment of the state of buildings in the aftermath of a disaster event is critical for an effective and timely response. For rapid damage assessment of buildings, the utility of remote sensing (RS) technology has been widely researched, with focus on a range of platforms and sensors. However, RS-based approaches still have limitations to assess structural integrity and the specific damage status of individual buildings. Structural integrity refers to the ability of a building to hold the entire structure. Consequently, ground-based assessment conducted by structural engineers and first responders is still required. This paper demonstrates the concept of mobile augmented reality (mAR) to improve performance of building damage and safety assessment in situ. Mobile AR provides a means to superimpose various types of reference or pre-disaster information (virtual data) on actual post-disaster building data (real buildings). To adopt mobile AR, this study defines a conceptual framework based on the level of complexity (LOC). The framework consists of four LOCs, and for each of these, the data types, required processing steps, AR implementation and use for damage assessment are described. Based on this conceptualization we demonstrate prototypes of mAR for both indoor and outdoor purposes. Finally, we conduct a user evaluation of the prototypes to validate the mAR approach for building damage and safety assessment.
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Jung, Minyoung, Junho Yeom, and Yongil Kim. "Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas." Remote Sensing 10, no. 11 (November 14, 2018): 1804. http://dx.doi.org/10.3390/rs10111804.
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Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.
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Bai, Yanbing, Bruno Adriano, Erick Mas, Hideomi Gokon, and Shunichi Koshimura. "Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images." Journal of Disaster Research 12, no. 2 (March 16, 2017): 259–71. http://dx.doi.org/10.20965/jdr.2017.p0259.
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Earthquake-induced building damage assessment is an indispensable prerequisite for disaster impact assessment, and the increasing availability of high-resolution Synthetic Aperture Radar (SAR) imagery has made it possible to construct damaged building inventories soon after earthquakes strike. However, the shortage of pre-seismic SAR datasets and the lack of available building footprint data pose challenges for rapid building damage assessment. Taking advantage of recent advances in machine learning algorithms, this study proposes an object-based building damage assessment methodology that uses only post-event SAR imagery. A Random Forest machine learning-based object classification, a simplified approach to the extraction of built-up areas, was developed and tested on two ALOS2/PALSAR-2 dual polarimetric SAR images acquired in affected areas soon after the 2015 Nepal earthquake. In addition, a series of texture metrics as well as the random scattering metric and reflection symmetry metric were found to significantly enhance classification accuracy. The feature selection was found to have a positive effect on overall performance. Moreover, the proposed Random Forest framework resulted in overall accuracies of 93% with a kappa coefficient of 0.885 when the object scale of 60 × 60 pixels and 15 features were adopted. A comparative experiment with the k-nearest neighbor framework demonstrated that the Random Forest framework is a significant step toward the achievement of a balanced, two-class classification.
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Tsuchimoto, Koji, Yasutaka Narazaki, and Billie F. Spencer. "Development and Validation of a Post-Earthquake Safety Assessment System for High-Rise Buildings Using Acceleration Measurements." Mathematics 9, no. 15 (July 26, 2021): 1758. http://dx.doi.org/10.3390/math9151758.
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After a major seismic event, structural safety inspections by qualified experts are required prior to reoccupying a building and resuming operation. Such manual inspections are generally performed by teams of two or more experts and are time consuming, labor intensive, subjective in nature, and potentially put the lives of the inspectors in danger. The authors reported previously on the system for a rapid post-earthquake safety assessment of buildings using sparse acceleration data. The proposed framework was demonstrated using simulation of a five-story steel building modeled with three-dimensional nonlinear analysis subjected to historical earthquakes. The results confirmed the potential of the proposed approach for rapid safety evaluation of buildings after seismic events. However, experimental validation on large-scale structures is required prior to field implementation. Moreover, an extension to the assessment of high-rise buildings, such as those commonly used for residences and offices in modern cities, is needed. To this end, a 1/3-scale 18-story experimental steel building tested on the shaking table at E-Defense in Japan is considered. The importance of online model updating of the linear building model used to calculate the Damage Sensitive Features (DSFs) during the operation is also discussed. Experimental results confirm the efficacy of the proposed approach for rapid post-earthquake safety evaluation for high-rise buildings. Finally, a cost-benefit analysis with respect to the number of sensors used is presented.
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Li, Yundong, Wei Hu, Han Dong, and Xueyan Zhang. "Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector." Applied Sciences 9, no. 6 (March 18, 2019): 1128. http://dx.doi.org/10.3390/app9061128.
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Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.
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Nex, F., E. Rupnik, I. Toschi, and F. Remondino. "Automated processing of high resolution airborne images for earthquake damage assessment." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 315–21. http://dx.doi.org/10.5194/isprsarchives-xl-1-315-2014.
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Emergency response ought to be rapid, reliable and efficient in terms of bringing the necessary help to sites where it is actually needed. Although the remote sensing techniques require minimum fieldwork and allow for continuous coverage, the established approaches rely on a vast manual work and visual assessment thus are time-consuming and imprecise. Automated processes with little possible interaction are in demand. This paper attempts to address the aforementioned issues by employing an unsupervised classification approach to identify building areas affected by an earthquake event. The classification task is formulated in the Markov Random Fields (MRF) framework and only post-event airborne high-resolution images serve as the input. The generated photogrammetric Digital Surface Model (DSM) and a true orthophoto provide height and spectral information to characterize the urban scene through a set of features. The classification proceeds in two phases, one for distinguishing the buildings out of an urban context (urban classification), and the other for identifying the damaged structures (building classification). The algorithms are evaluated on a dataset consisting of aerial images (7 cm GSD) taken after the Emilia-Romagna (Italy) earthquake in 2012.
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Kalantar, Bahareh, Naonori Ueda, Husam A. H. Al-Najjar, and Alfian Abdul Halin. "Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images." Remote Sensing 12, no. 21 (October 28, 2020): 3529. http://dx.doi.org/10.3390/rs12213529.
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In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.
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Zucca, Marco, Emanuele Reccia, Nicola Longarini, and Antonio Cazzani. "Seismic Assessment and Retrofitting of an Historical Masonry Building Damaged during the 2016 Centro Italia Seismic Event." Applied Sciences 12, no. 22 (November 20, 2022): 11789. http://dx.doi.org/10.3390/app122211789.
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The preservation and definition of the correct retrofitting interventions of historic masonry buildings represents a relevant topic nowadays, especially in a country characterized by high seismicity zones. Considering the Italian Cultural Heritage, most of these buildings are constructed in ancient unreinforced masonry (URM) and showed a high level of vulnerability during the recent 2009 (L’Aquila), 2012 (Emilia Romagna) and 2016 (Centro Italia) earthquakes. In this paper, the seismic assessment of an historic masonry building damaged during 2016 Centro Italia seismic event is presented considering different types of retrofitting interventions. Starting from the results obtained by the post-earthquake survey, different finite element models have been implemented to perform linear and non-linear analyses useful to understand the seismic behaviour of the building and to define the appropriate retrofitting interventions. In particular, reinforced plaster layer and cement-based grout injections have been applied in each masonry wall of the building in order to improve their horizontal load-bearing capacity, and an additional wall made with Poroton blocks and M10 cement mortar has been built adjacent to the central stairwell. In addition, in view of the need to replace the roof seriously damaged during the seismic event, a cross-laminated roof solution characterized by a thickness equal to 14 cm (composed by seven layers, each 2 cm thick) has been proposed. The results show that the proposed retrofitting interventions have led to a significant improvement in the seismic behaviour of the building.
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Saito, Keiko, Robin Spence, and Terence A. de C Foley. "Visual Damage Assessment using High-Resolution Satellite Images following the 2003 Bam, Iran, Earthquake." Earthquake Spectra 21, no. 1_suppl (December 2005): 309–18. http://dx.doi.org/10.1193/1.2101107.
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Visual interpretation of the building damage distribution in Bam, Iran, caused by the earthquake on 26 December 2003 has been carried out using pre- and post-earthquake QuickBird panchromatic high-resolution satellite images to produce a damage map. Two experienced interpreters carried out the assessments, and their results were compared to analyze the reasons for discrepancies likely to occur from interpretations by different interpreters. The first damage interpretation was carried out on the post-earthquake image, whereas the second interpretation compared the pre- and post-earthquake images. The analysis revealed that when using only the post-earthquake image, interpreters tend to underestimate the levels of damage, since both interpreters assigned higher damage levels when the pre- and post-earthquake image were compared than when only using the post-earthquake image. The absolute difference in the damage levels the two interpreters assigned in the post-only assessment and pre-and post-event comparison assessment remained the same.
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Zhan, Yihao, Wen Liu, and Yoshihisa Maruyama. "Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake." Remote Sensing 14, no. 4 (February 18, 2022): 1002. http://dx.doi.org/10.3390/rs14041002.
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Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.
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Lemoine, G., C. Corbane, C. Louvrier, and M. Kauffmann. "Intercomparison and validation of building damage assessments based on post-Haiti 2010 earthquake imagery using multi-source reference data." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (April 22, 2013): 1445–86. http://dx.doi.org/10.5194/nhessd-1-1445-2013.
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Abstract. The Haiti 2010 earthquake is one of the first major disasters in which very high resolution satellite and airborne imagery was embraced to delineate the event impact. Several rapid mapping initiatives exploited post-earthquake satellite and airborne imagery to produce independent point feature sets marking the damage grade of affected buildings. Despite the obvious potential of the satellite remote sensing technology in providing damage figures, the scale and complexity of the urban structures in Port-au-Prince cause overall figures and patterns of the damage assessments to yield a rather poor representation of the true damage extent. The higher detail airborne imagery performs much better as confirmed by different validation studies carried out in the last two years. In this paper, in addition to the review and analysis of the different validation works, we investigate the quality of damage assessment derived by different activities through a simple intercomparison and a validation using a complete building ground survey. The results show that the identification of building damage from aerial imagery provides a realistic estimate of the spatial pattern and intensity of building damage.
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Polegato, Rosemary, and Rune Bjerke. "Looking forward: anticipation enhances service experiences." Journal of Services Marketing 33, no. 2 (April 8, 2019): 148–59. http://dx.doi.org/10.1108/jsm-02-2018-0064.
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Purpose This paper aims to explore the nature and relationships among the dimensions that constitute expectations, anticipation and post-experience assessment of cultural events, before and after an aesthetic experience, namely, a live Norwegian opera or ballet performance. Design/methodology/approach A triangulation approach is used to combine qualitative and quantitative analyses. Quantitative data collection was conducted at the site before and after a performance experience. Findings Expectations, anticipation and post-experience assessment are found to be multi-dimensional. Expectations and anticipation are identified as distinct constructs. Three dimensions of expectations of quality are extrinsic cues: building and functional attributes, available services and level of employee service. In addition, two dimensions of pre-experience anticipation are identified: anticipation of information gathering activities and anticipation of the event. Post-experience assessment has two dimensions: satisfaction and pride in the building. Two post-experience associations are enthusiasm and inclusiveness. Anticipation of the event and enthusiasm, not expectations, are found to be predictors of satisfaction. Research limitations/implications An understanding of the role of anticipation in consumer engagement and satisfaction with aesthetic experiences could be broadened and enriched by studies that include other service or arts disciplines and within a more complex model of consumer engagement. Originality/value Anticipation is a significant pre-experience phenomenon. Enthusiasm is identified as a post-experience association.
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Ripepe, Maurizio, Giorgio Lacanna, Pauline Deguy, Mario de Stefano, Valentina Mariani, and Marco Tanganelli. "Large-Scale Seismic Vulnerability Assessment Method for Urban Centres. An Application to the City of Florence." Key Engineering Materials 628 (August 2014): 49–54. http://dx.doi.org/10.4028/www.scientific.net/kem.628.49.
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The seismic vulnerability assessment of a building requires a comprehensive knowledge of both building structural features and soils geophysical parameters. To achieve a vulnerability assessment at the urban scale a large amount of data would be necessary, with a consequent involvement of time and economical resources. The aim of this paper is hence to propose a simplified procedure to evaluate the seismic vulnerability of urban centres and possible seismic damage scenarios in order to identify critical areas and/or building typologies to plan future actions of seismic risk mitigation and prevention. The procedure is applied to the outstanding case study of the city of Florence. The research is based on the definition of major building typologies related to construction periods and type of the structural system (masonry or reinforced concrete), the identification of a set of sample buildings, the analysis of the dynamic behaviour and the evaluation of a vulnerability index with an expeditious approach. The obtained results allow to define potential vulnerability and post-event damage scenarios related to the expected levels of peak ground acceleration.
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Kaplan, Onur, and Gordana Kaplan. "Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake." Buildings 12, no. 1 (December 26, 2021): 14. http://dx.doi.org/10.3390/buildings12010014.
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Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such buildings to collapse. The potential of the response spectrum concept for being associated with satellite-based remote sensing data for post-earthquake structural damage estimation was investigated in this study. In this respect, a response spectra-based post-earthquake structural damage estimation method aided by satellite-based remote sensing data was proposed to classify the buildings after an earthquake by prioritizing them based on their expected damage levels, in order to speed up the damage assessment process of critical buildings that can cause casualties in a possible strong aftershock. A case study application was implemented in the Bayrakli region in Izmir, Turkey, the most affected area by the Samos earthquake, on 30 October 2020. The damage estimations made in this research were compared with the in situ damage assessment reports prepared by the Republic of Turkey Ministry of Environment and Urbanization experts. According to the accuracy assessment results, the sensitivity of the method is high (91%), and the necessary time spent by the in situ damage assessment teams to detect the critical buildings would have been significantly reduced for the study area.
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Tian, Jiaojiao, Allan A. Nielsen, and Peter Reinartz. "Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs." International Journal of Image and Data Fusion 6, no. 2 (March 30, 2015): 155–69. http://dx.doi.org/10.1080/19479832.2014.1001879.
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Saito, Keiko, Robin J. S. Spence, Christopher Going, and Michael Markus. "Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study following the 26 January 2001 Gujarat Earthquake." Earthquake Spectra 20, no. 1 (February 2004): 145–69. http://dx.doi.org/10.1193/1.1650865.
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Newly available optical satellite images with 1-m ground resolution such as IKONOS mean that rapid postdisaster damage assessment might be made over large areas. Such surveys could be of great value to emergency management and post-event recovery operations and have particular promise for earthquake areas, where damage distribution is often very uneven. In this paper three satellite images taken before and after the 26 January 2001 Gujarat earthquake were studied for damage assessment purposes. The images comprised a post-earthquake cover of the city of Bhuj, which was close to the epicenter, and pre- and post-earthquake cover of the city Ahmedabad. The assessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite image of Bhuj was compared with detailed ground photos of 28 severely damaged buildings taken at about the same time as the satellite image, to investigate the levels and types of damage that can and cannot be identified. In the second experiment, the whole city center of Bhuj was damage mapped using only the satellite image. This was subsequently compared with a map produced from a building-by-building damage survey. In the third experiment, pre- and post-earthquake images for a large area of Ahmedabad were compared and totally collapsed buildings were identified. These sites were subsequently visited to confirm the accuracy of the observations. The experiment results indicate that rapid visual screening can identify areas of heavy damage and individual collapsed buildings, even when comparative cover does not exist. The need to develop a tool with direct application to support emergency response is discussed.
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Nazarian, Ebrahim, Todd Taylor, Tian Weifeng, and Farhad Ansari. "Machine-learning-based approach for post event assessment of damage in a turn-of-the-century building structure." Journal of Civil Structural Health Monitoring 8, no. 2 (March 7, 2018): 237–51. http://dx.doi.org/10.1007/s13349-018-0275-6.
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Maharjan, Sony, and Shobha Shrestha. "An Assessment of Earthquake Risk in Thecho of Kathmandu Valley Nepal: Scenario and Reality." Geographical Journal of Nepal 11 (April 3, 2018): 127–36. http://dx.doi.org/10.3126/gjn.v11i0.19553.
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Natural disaster cannot be stopped but its effect can be minimized or avoided by adopting technology and necessary human adjustment. Earthquake is a natural event which occurs without early warning signs. Computer based earthquake scenarios are used worldwide to describe and estimate the damage from potential earthquakes. The current study is an attempt to explore potential risk with respect to physical infrastructure and assess modeled and actual physical damage and human loss caused by different earthquake scenario and actual 2015 earthquake event in Thecho of Kathmandu valley. The earthquake scenario is based on two nearest fault lines. Risk Assessment Tools for the Diagnosis of Urban Seismic Risk (RADIUS) method has been applied for estimation of potential building damage and casualties..The research has adopted integrated approach using secondary and primary data sources such as field observation, key informant survey and building survey through purposive random sampling.The study found that potential building damage estimated by RADIUS for Gorkha 2015 earthquake scenario and North-west (Khokana) are lower than the actual post-earthquake assessment whereas North earthquake scenario resulted higher loss. Actual damage caused by 2015 earthquake compared to modeled damage from RADIUS is found higher because additional damaged were made by successive aftershocks. Spatial distribution of potential building damage for earthquake scenarios and actual 2015 earthquake event is also variable. North-Nuwakot Earthquake Scenario resulted more hazardous than the North-Khokana scenario though the location of epicenter is relatively farther with high intensity. The study concluded that though earthquake occurrence and disaster is still less predictable risk assessment tools like RADIUS and mitigation measures based on such is important for reducing risk of earthquake disaster.The Geographical Journal of NepalVol. 11: 127-136, 2018
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Foulser-Piggott, Roxane, Robin Spence, Ron Eguchi, and Andrew King. "Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake." Earthquake Spectra 32, no. 1 (February 2016): 611–31. http://dx.doi.org/10.1193/051214eqs067m.
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This study explores the performance of GEOCAN, a remote-sensing and crowdsourcing platform for assessing earthquake damage, by using geo-referenced ground-based damage assessments. This paper discusses methods for the application of remote sensing in post-earthquake damage assessment and reports on a GEOCAN crowd-sourcing study following the 22 February 2011 Christchurch event and its validation using field studies. It describes the principal data sets used, discusses in detail the problems of validation, and considers the extent of omission and commission errors. It is clear that although commission errors in the GEOCAN damage estimation are low, the omission error is significant (64%); the extent of these and the causal factors are analyzed with a decision model. The results show that the image-based analysis in this case does not reproduce the spatial pattern or magnitude of the damage impact. Finally, recommendations to improve the performance of GEOCAN in subsequent deployments are made.
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Bai, Yanbing, Bruno Adriano, Erick Mas, and Shunichi Koshimura. "Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery." Earthquake Spectra 33, no. 1_suppl (December 2017): 185–95. http://dx.doi.org/10.1193/121516eqs232m.
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This paper takes the 2015 Nepal earthquake as a case study to explore the use of post-event dual polarimetric synthetic aperture radar images for earthquake damage assessment. The radar scattering characteristics of damaged and undamaged urban areas were compared by using polarimetric features derived from PALSAR-2 and Sentinel-1 images, and the results showed that distinguishing between damaged and undamaged urban areas with a single polarimetric feature is challenging. A split-based image analysis, feature selection, and supervised classification were employed on a PALSAR-2 image. The texture features derived from the intensity of cross-polarization show higher correlations with the damage class. Additionally, feature selection revealed a positive influence on the overall performance. Employing 70% of the data for training and 30% data for testing, the support vector machine classifier achieved an accuracy of 80.5% compared with the reference data generated from the damage map that was provided by the United Nations Operational Satellite Applications Programme.
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Matin, Sahar S., and Biswajeet Pradhan. "Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)." Sensors 21, no. 13 (June 30, 2021): 4489. http://dx.doi.org/10.3390/s21134489.
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Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.
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Hill, Arleen, John Bevington, Rachel Davidson, Stephanie Chang, Ronald Eguchi, Beverley Adams, Susan Brink, et al. "Community-Scale Damage, Disruption, and Early Recovery in the 2010 Haiti Earthquake." Earthquake Spectra 27, no. 1_suppl1 (October 2011): 431–46. http://dx.doi.org/10.1193/1.3624964.
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This study seeks to assess the levels of community-scale building damage and socioeconomic disruption following the January 2010 Haiti earthquake. Damage and disruption were analyzed for pre-event, post-event, and early recovery time periods in seven Haitian communities—three inside and four outside Port-au-Prince. Damage datasets from the Global Earth Observation-Catastrophe Assessment Network (GEO-CAN) postdisaster assessment were combined with analyses of fine-resolution satellite and aerial imagery to quantify building damage and recovery status, and were verified with field data. Disruption was assessed using community-level data obtained from interviews conducted in May 2010 with community leaders, NGOs, and government utility providers. The data pertain to 11 sectors, including shelter, livelihoods, and social networks. The findings document severe disruption and uneven restoration four months after the earthquake. Disruption showed little correlation with physical damage. Observations suggest that the impacts of the earthquake must be understood in the context of chronic disruption, and many consequences of the earthquake are merely deferred during recovery.
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Fritz, Samantha, Ian Milligan, Nick Ruest, and Jimmy Lin. "Building community at distance: a datathon during COVID-19." Digital Library Perspectives 36, no. 4 (August 3, 2020): 415–28. http://dx.doi.org/10.1108/dlp-04-2020-0024.
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Purpose This paper aims to use the experience of an in-person event that was forced to go virtual in the wake of COVID-19 as an entryway into a discussion on the broader implications around transitioning events online. It gives both practical recommendation to event organizers as well as broader reflections on the role of digital libraries during the COVID-19 pandemic and beyond. Design/methodology/approach The authors draw on their personal experiences with the datathon, as well as a comprehensive review of literature. The authors provide a candid assessment of what approaches worked and which ones did not. Findings A series of best practices are provided, including factors for assessing whether an event can be run online; the mixture of synchronous versus asynchronous content; and important technical questions around delivery. Focusing on a detailed case study of the shift of the physical team-building exercise, the authors note how cloud-based platforms were able to successfully assemble teams and jumpstart online collaboration. The existing decision to use cloud-based infrastructure facilitated the event’s transition as well. The authors use these examples to provide some broader insights on meaningful content delivery during the COVID-19 pandemic. Originality/value Moving an event online during a novel pandemic is part of a broader shift within the digital libraries’ community. This paper thus provides a useful professional resource for others exploring this shift, as well as those exploring new program delivery in the post-pandemic period (both due to an emphasis on climate reduction as well as reduced travel budgets in a potential period of financial austerity).
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Forcellini, Davide. "The Role of Climate Change in the Assessment of the Seismic Resilience of Infrastructures." Infrastructures 6, no. 5 (May 18, 2021): 76. http://dx.doi.org/10.3390/infrastructures6050076.
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Climate change is modifying scientific attitudes toward pre- and post-event assessments of natural hazards. Unprecedented levels of destruction need renewed focus on addressing and protecting communities forcing the decision makers to change their attention to vulnerability and risk assessment. In particular, society and economy rely heavily on infrastructures, as fundamental links for movement of goods and people, and are extremely vulnerable to multiple hazards (such as droughts, floods, storms, and coastal hazards). In this regard, resilience quantifies the recovery time and procedures to facilitate and enhance pre-hazard and post-hazard event mitigation and emergency response strategies of systems and entire communities. Resilience calculation depends on two important contributions: loss and recovery models that need to consider the effects of climate change. This paper aims to propose a methodology that implements the most recent approaches to assess climate change inside the traditional framework of resilience. The proposed framework is then applied to a case study of a bridge.
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Fuchs, S., K. Heiss, and J. Hübl. "Towards an empirical vulnerability function for use in debris flow risk assessment." Natural Hazards and Earth System Sciences 7, no. 5 (August 30, 2007): 495–506. http://dx.doi.org/10.5194/nhess-7-495-2007.
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Abstract. In quantitative risk assessment, risk is expressed as a function of the hazard, the elements at risk and the vulnerability. From a natural sciences perspective, vulnerability is defined as the expected degree of loss for an element at risk as a consequence of a certain event. The resulting value is dependent on the impacting process intensity and the susceptibility of the elements at risk, and ranges from 0 (no damage) to 1 (complete destruction). With respect to debris flows, the concept of vulnerability – though widely acknowledged – did not result in any sound quantitative relationship between process intensities and vulnerability values so far, even if considerable loss occurred during recent years. To close this gap and establish this relationship, data from a well-documented debris flow event in the Austrian Alps was used to derive a quantitative vulnerability function applicable to buildings located on the fan of the torrent. The results suggest a second order polynomial function to fit best to the observed damage pattern. Vulnerability is highly dependent on the construction material used for exposed elements at risk. The buildings studied within the test site were constructed by using brick masonry and concrete, a typical design in post-1950s building craft in alpine countries. Consequently, the presented intensity-vulnerability relationship is applicable to this construction type within European mountains. However, a wider application of the presented method to additional test sites would allow for further improvement of the results and would support an enhanced standardisation of the vulnerability function.
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Pittore, Massimiliano, Laura Graziani, Alessandra Maramai, Michael Haas, Stefano Parolai, and Andrea Tertulliani. "Bayesian Estimation of Macroseismic Intensity from Post-Earthquake Rapid Damage Mapping." Earthquake Spectra 34, no. 4 (November 2018): 1809–28. http://dx.doi.org/10.1193/112517eqs241m.
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The seismological community acknowledges the essential contribution of macroseismic assessment to the compilation of the seismic catalogs used for seismic hazard assessment. Furthermore, macroseismic observations are routinely employed by civil protection authorities in the aftermath of damaging events to improve their decision making. In this paper, we describe a novel methodology for the rapid, probabilistic estimation of macroseismic intensity in the epicentral area of a major event according to the European Macroseismic scale. The methodology includes mobile mapping and a collaborative online platform for rapid post earthquake reconnaissance. A Bayesian scheme is proposed to integrate direct damage observations and prior information, allowing consideration of ancillary data and expert judgment. According to a feasibility study carried out in the area affected by the 2016 Amatrice (Central Italy) earthquake, the proposed methodology should provide a reliable estimation of intensity, efficiently integrating further post earthquake building damage surveys.
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Colonna, Silvia, Stefania Imperatore, Maria Zucconi, and Barbara Ferracuti. "Post-Seismic Damage Assessment of a Historical Masonry Building: The Case Study of a School in Teramo." Key Engineering Materials 747 (July 2017): 620–27. http://dx.doi.org/10.4028/www.scientific.net/kem.747.620.
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The historical masonry buildings are characterised by a great vulnerability regard the seismic action, as the recent events occurred in Central Italy have highlighted. During the seismic emergency the authors, in collaboration with the Civil Protection Department as part of the ReLUIS activities, have carried out usability inspections, analysing also the case study described in this paper. The structure, a school in Teramo, was already affected by previously seismic damages and it has been highly involved by the seismic events abovementioned. In this work the results of first inspection, reported in the AeDES form, and a more accurate visual inspection are presented in terms of detection of the crack patterns and evaluation of the seismic damages index. Moreover the vulnerability index has been calculated according to the GNDT 2° level method. The vulnerability index is finally used to calculate the damage index expected for the seismic intensity registered during the seismic event of October 30, 2016, and compared with the observed post-seismic damage level.
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Lu, Xinzheng, Qingle Cheng, Zhen Xu, Yongjia Xu, and Chujin Sun. "Real-Time City-Scale Time-History Analysis and Its Application in Resilience-Oriented Earthquake Emergency Responses." Applied Sciences 9, no. 17 (August 24, 2019): 3497. http://dx.doi.org/10.3390/app9173497.
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The resilience of cities has received worldwide attention. An accurate and rapid assessment of seismic damage, economic loss, and post-event repair time can provide an important reference for emergency rescue and post-earthquake recovery. Based on city-scale nonlinear time-history analysis (THA) and regional seismic loss prediction, a real-time city-scale time-history analysis method is proposed in this work. In this method, the actual ground motion records obtained from seismic stations are input into the building models of the earthquake-stricken area, and the nonlinear time-history analysis of these models is subsequently performed using a high-performance computing platform. The seismic damage to the buildings in the target region subjected to this earthquake is evaluated according to the analysis results. The economic loss and repair time of the earthquake-stricken areas are calculated using the engineering demand parameters obtained from the time-history analysis. A program named, “Real-time Earthquake Damage Assessment using City-scale Time-history analysis” (“RED-ACT” for short) was developed to automatically implement the above workflow. The method proposed in this work has been applied in many earthquake events, and provides a useful reference for scientific decision making for earthquake disaster relief, which is of great significance to enhancing the resilience of earthquake-stricken areas.
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Laudan, Jonas, Viktor Rözer, Tobias Sieg, Kristin Vogel, and Annegret H. Thieken. "Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods." Natural Hazards and Earth System Sciences 17, no. 12 (December 6, 2017): 2163–79. http://dx.doi.org/10.5194/nhess-17-2163-2017.
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Abstract. Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-flood-prone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.
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Spasenovic, K., D. Carrion, and F. Migliaccio. "POTENTIAL OF GEOLOCATED CROWDSOURCED IMAGE POSTS IN PREDICTING AN EARLY ESTIMATE OF THE PATTERNS OF STRUCTURAL DAMAGE FOLLOWING A HURRICANE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 291–97. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-291-2020.
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Abstract. During a disaster, the activity of the crowd represents a very valuable source of the on-the-ground conditions shared by the affected citizens. The approach, presented in the paper, explores the relationship between the spatial distribution of crowdsourced image posts and damaged buildings in order to understand the potential of modelling the spatial distribution of damaged buildings based on geolocated images. The posts related to the hurricane Michael that happened in the United States in October 2018, showing the building damage of Panama City, have been collected by NAPSG Foundation and GISCorps volunteers. The building damage assessment, based on the analysis of high-resolution post-event imagery, has been performed by FEMA. Exploring the two available independent point datasets, the spatial pattern of each individual dataset has been analysed and furthermore the spatial relationship between them has been explored. A set of spatial statistics has been performed with R software. For this purpose, the distance-based methods have been used, that consider the mutual position of points to describe the patterns. The results shown the spatial relationship between the crowdsourced photos and different damage types. Furthermore, potential of crowdsourced images for improving the awareness of the structural damage after the hurricane have been discussed.
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Hajeb, Masoud, Sadra Karimzadeh, and Masashi Matsuoka. "SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan." Applied Sciences 10, no. 24 (December 14, 2020): 8932. http://dx.doi.org/10.3390/app10248932.
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The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%.
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Atefi, Mujeeb Rahman, and Hiroyuki Miura. "Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan." Remote Sensing 14, no. 15 (July 29, 2022): 3647. http://dx.doi.org/10.3390/rs14153647.
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On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.
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Kean, J. W., D. M. Staley, J. T. Lancaster, F. K. Rengers, B. J. Swanson, J. A. Coe, J. L. Hernandez, A. J. Sigman, K. E. Allstadt, and D. N. Lindsay. "Inundation, flow dynamics, and damage in the 9 January 2018 Montecito debris-flow event, California, USA: Opportunities and challenges for post-wildfire risk assessment." Geosphere 15, no. 4 (June 7, 2019): 1140–63. http://dx.doi.org/10.1130/ges02048.1.
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Abstract Shortly before the beginning of the 2017–2018 winter rainy season, one of the largest fires in California (USA) history (Thomas fire) substantially increased the susceptibility of steep slopes in Santa Barbara and Ventura Counties to debris flows. On 9 January 2018, before the fire was fully contained, an intense burst of rain fell on the portion of the burn area above Montecito, California. The rainfall and associated runoff triggered a series of debris flows that mobilized ∼680,000 m3 of sediment (including boulders >6 m in diameter) at velocities up to 4 m/s down coalescing urbanized alluvial fans. The resulting destruction (including 23 fatalities, at least 167 injuries, and 408 damaged homes) underscores the need for improved understanding of debris-flow runout in the built environment, and the need for a comprehensive framework to assess the potential loss from debris flows following wildfire. We present observations of the inundation, debris-flow dynamics, and damage from the event. The data include field measurements of flow depth and deposit characteristics made within the first 12 days after the event (before ephemeral features of the deposits were lost to recovery operations); an inventory of building damage; estimates of flow velocity; information on flow timing; soil-hydrologic properties; and post-event imagery and lidar. Together, these data provide rare spatial and dynamic constraints for testing debris-flow runout models, which are needed for advancing post-fire debris-flow hazard assessments. Our analysis also outlines a framework for translating the results of these models into estimates of economic loss based on an adaptation of the U.S. Federal Emergency Management Agency’s Hazus model for tsunamis.
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Tian, J., L. Metzlaff, P. d’Angelo, and P. Reinartz. "REGION-BASED BUILDING ROOFTOP EXTRACTION AND CHANGE DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 903–8. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-903-2017.
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Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs) to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.