Building damage assessment scale tailored to remote sensing vertical imagery

Cotrufo, Silvana; Sandu, Constantin; Tonolo, Fabio Giulio; Boccardo, Piero

doi:10.1080/22797254.2018.1527662

Cited by 31 publications

(16 citation statements)

References 7 publications

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“…This is a newly developed scale, which was specially designed to be generalized for damage caused by different types of natural hazards and to be well-suited for annotation from aerial imagery. Commonly used scales, such as the EMS-98 [62], only cover one type of damage and categorize according to damage that is not always visible from the sky [46,63]. Besides the location of the buildings and their degree of damage, the xBD dataset does not include other information on characteristics such as the type of buildings.…”

Section: Datamentioning

confidence: 99%

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

et al. 2020

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Automated classification of building damage in remote sensing images enables the rapid and spatially extensive assessment of the impact of natural hazards, thus speeding up emergency response efforts. Convolutional neural networks (CNNs) can reach good performance on such a task in experimental settings. How CNNs perform when applied under operational emergency conditions, with unseen data and time constraints, is not well studied. This study focuses on the applicability of a CNN-based model in such scenarios. We performed experiments on 13 disasters that differ in natural hazard type, geographical location, and image parameters. The types of natural hazards were hurricanes, tornadoes, floods, tsunamis, and volcanic eruptions, which struck across North America, Central America, and Asia. We used 175,289 buildings from the xBD dataset, which contains human-annotated multiclass damage labels on high-resolution satellite imagery with red, green, and blue (RGB) bands. First, our experiments showed that the performance in terms of area under the curve does not correlate with the type of natural hazard, geographical region, and satellite parameters such as the off-nadir angle. Second, while performance differed highly between occurrences of disasters, our model still reached a high level of performance without using any labeled data of the test disaster during training. This provides the first evidence that such a model can be effectively applied under operational conditions, where labeled damage data of the disaster cannot be available timely and thus model (re-)training is not an option.

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Section: Datamentioning

confidence: 99%

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

et al. 2020

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“…While Copernicus EMS uses five damage classes (Grünthal, 1998), the BAR Methodology uses four classes ("critical visible damage"; "significant visible damage"; "minimal visible damage" and "no visible damage"); the UNOSAT classification uses a binary approach ("damaged"; "not damaged"). An exhaustive analysis of the different damage scales used for building damage assessment by the main satellite-based emergency mapping service has been discussed (Cotrufo et al, 2018), stating that different damage classes and detailed interpretation guidelines with operational examples are crucial for assuring the suitable analysis of the analyzed data.…”

Section: Geomatics Tools For Damage Assessmentmentioning

confidence: 99%

“…These sources are unfortunately not always up to date; for this reason, when building vector footprints are not available or outdated, it is possible to exploit other kinds of pre-event data, such as VHR satellite image or aerial image that (if relatively recent) allows a manual interpretation of the building's boundaries (Li & Tang, 2018). Some experience (Cotrufo et al, 2018) underlined that it is impossible to directly apply the damage classification scales proposed (Grünthal, 1998) for addressing slight structural damages using remotely sensed images. Even if satellite images can achieve sub-meter resolution, there are still issues in identifying partially damaged buildings; for this reason, data integration from UAV-derived information remains crucial (Li & Tang, 2018).…”

Section: Geomatics Tools For Damage Assessmentmentioning

confidence: 99%

Deep Learning for Automatic Building Damage Assessment: Application in Post-Disaster Scenarios Using Uav Data

Calantropio

Chiabrando

Codastefano

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. During the last few years, the technical and scientific advances in the Geomatics research field have led to the validation of new mapping and surveying strategies, without neglecting already consolidated practices. The use of remote sensing data for damage assessment in post-disaster scenarios underlined, in several contexts and situations, the importance of the Geomatics applied techniques for disaster management operations, and nowadays their reliability and suitability in environmental emergencies is globally recognized. In this paper, the authors present their experiences in the framework of the 2016 earthquake in Central Italy and the 2019 Cyclone Idai in Mozambique. Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid production of cartography to be used in emergency response operations. The performed analyses have been presented, and the strengths and weaknesses of the employed methods and techniques have been outlined. In conclusion and based on the authors' experience, some operational suggestions and best practices are provided and future research perspectives within the same research topic are introduced.

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“…1: Geographical distribution of brick kilns in "Brick-Kiln-Belt" of South Asia [14], [15], [16], [17], [18], [19]. estimation [7] and damage assessment due to natural and manmade disasters [8].…”

Section: Introductionmentioning

confidence: 99%

Kiln-Net: A Gated Neural Network for Detection of Brick Kilns in South Asia

Nazir

Mian

Sohail

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The availability of high-resolution satellite imagery has enabled several new applications such as identification of brick kilns for the elimination of modern-day slavery. This requires automated analysis of approximately 1, 551, 997 km 2 area within the "Brick-Kiln-Belt" of South Asia. Although modern machine learning techniques have achieved high accuracy for a wide variety of applications, problems involving large-scale analysis using high-resolution satellite imagery requires both accuracy as well as computational efficiency. We propose a coarse-to-fine strategy consisting of an inexpensive classifier and a detector which work in tandem to achieve high accuracy at low computational cost. More specifically, we propose a two-stage gated neural network architecture called Kiln-Net. At the first stage, imagery is classified using the ResNet-152 model which filters out over 99% of irrelevant data. At the second stage, a YOLOv3-based object detector is applied to find the precise location of each brick kiln in the candidate regions. The dataset, named Asia14, consisting of 14, 000 Digital Globe RGB images and 14 categories is also developed to train the proposed Kiln-Net architecture. Our proposed network architecture is evaluated on approximately 3, 300 km 2 region (3, 37, 723 image patches) from 14 different cities in five different countries of South Asia. It outperforms state-of-the-art methods employed for the recognition of brick kilns and achieved an accuracy of 99.96% and average F1 score of 0.91. To the best of our knowledge, it is also 20x faster than existing methods.

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Building damage assessment scale tailored to remote sensing vertical imagery

Cited by 31 publications

References 7 publications

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment

Deep Learning for Automatic Building Damage Assessment: Application in Post-Disaster Scenarios Using Uav Data

Kiln-Net: A Gated Neural Network for Detection of Brick Kilns in South Asia

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