Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives

Dell’Acqua, Fabio; Gamba, Paolo

doi:10.1109/jproc.2012.2196404

Cited by 175 publications

(96 citation statements)

References 41 publications

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“…Figure 2 shows the impact of earthquake damage on two selected textural and structural features. Entropy, energy, dissimilarity, and homogeneity are all second order texture features derived from the GLCM that have been correlated with damage or used as proxies for damage in previous studies [35,[37][38][39]. In order to reduce dimensionality and eliminate redundancy, the two consistently correlated GLCM features of Entropy (a measure of gray level randomness) and Dissimilarity (a measure of gray level difference (the square of contrast)) were chosen as texture inputs.…”

Section: Texture and Structurementioning

confidence: 99%

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

2016

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Abstract:Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (M w ) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.

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Section: Texture and Structurementioning

confidence: 99%

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

2016

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“…(2) Split the gradient image into AˆB cells. For each cell, compute the histogram of gradient orientation by adding the magnitude of the gradient to its corresponding orientation bin.…”

Section: (A)mentioning

confidence: 99%

“…Direct manual field inspection is labor intensive, time consuming, and cannot assess the damages in inaccessible areas. Remote sensing technology is the most predominant and early source to provide data for performing such assessments, either manually or using automated image analysis procedures [1,2]. Various kinds of remote sensing data such as optical, synthetic aperture radar (SAR), and LiDAR are being used for the damage assessment process [1].…”

Section: Introductionmentioning

confidence: 99%

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

et al. 2016

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Automatic post-disaster mapping of building damage using remote sensing images is an important and time-critical element of disaster management. The characteristics of remote sensing images available immediately after the disaster are not certain, since they may vary in terms of capturing platform, sensor-view, image scale, and scene complexity. Therefore, a generalized method for damage detection that is impervious to the mentioned image characteristics is desirable. This study aims to develop a method to perform grid-level damage classification of remote sensing images by detecting the damage corresponding to debris, rubble piles, and heavy spalling within a defined grid, regardless of the aforementioned image characteristics. The Visual-Bag-of-Words (BoW) is one of the most widely used and proven frameworks for image classification in the field of computer vision. The framework adopts a kind of feature representation strategy that has been shown to be more efficient for image classification-regardless of the scale and clutter-than conventional global feature representations. In this study supervised models using various radiometric descriptors (histogram of gradient orientations (HoG) and Gabor wavelets) and classifiers (SVM, Random Forests, and Adaboost) were developed for damage classification based on both BoW and conventional global feature representations, and tested with four datasets. Those vary according to the aforementioned image characteristics. The BoW framework outperformed conventional global feature representation approaches in all scenarios (i.e., for all combinations of feature descriptors, classifiers, and datasets), and produced an average accuracy of approximately 90%. Particularly encouraging was an accuracy improvement by 14% (from 77% to 91%) produced by BoW over global representation for the most complex dataset, which was used to test the generalization capability.

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“…Remote sensing is an effective tool for detecting damaged areas because it can be used to document damage to large areas without direct access to the affected area (Yamazaki and Matsuoka, 2007;Rathje and Adams, 2008;Dell'Acqua and Gamba, 2012). Immense improvement to the accessibility of remote-sensing imagery data and geospatial data processing tools has been achieved over the last several years (Vuolo et al, 2016;Korosov et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

Moya¹,

Yamazaki

Wen

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. The 2016 Kumamoto earthquake sequence was triggered by an M w 6.2 event at 21:26 on 14 April. Approximately 28 h later, at 01:25 on 16 April, an M w 7.0 event (the mainshock) followed. The epicenters of both events were located near the residential area of Mashiki and affected the region nearby. Due to very strong seismic ground motion, the earthquake produced extensive damage to buildings and infrastructure. In this paper, collapsed buildings were detected using a pair of digital surface models (DSMs), taken before and after the 16 April mainshock by airborne light detection and ranging (lidar) flights. Different methods were evaluated to identify collapsed buildings from the DSMs. The change in average elevation within a building footprint was found to be the most important factor. Finally, the distribution of collapsed buildings in the study area was presented, and the result was consistent with that of a building damage survey performed after the earthquake.

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Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives

Cited by 175 publications

References 41 publications

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

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