Context-based mapping of damaged buildings from high-resolution optical satellite images

Vu, Tuong Thuy; Ban, Yifang

doi:10.1080/01431161003727697

Cited by 54 publications

(30 citation statements)

References 11 publications

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“…In this case, textural information is known as a powerful tool in image analysis. A wide range of studies have applied textural information in image-based damage assessment (Rezaeian, 2010;Vu and Ban, 2010).…”

Section: Texture Analysismentioning

confidence: 99%

“…In comparison with airborne data, rapid access and continuous coverage of satellite images have allowed most researchers to apply these images for damage assessment (Brunner et al, 2010;Vu and Ban, 2010;Yamazaki and Matsuoka, 2007); while other 456 H. Rastiveis et al: A fuzzy decision making system for building damage map creation researchers have used aerial images for damage assessment (Li et al, 2008;Rezaeian, 2010;Thomas, 2010;Turker and Sumer, 2008).…”

Section: Introductionmentioning

confidence: 99%

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A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Rastiveis

Samadzadegan

Reinartz

2013

Nat. Hazards Earth Syst. Sci.

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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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Section: Texture Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Rastiveis

Samadzadegan

Reinartz

2013

Nat. Hazards Earth Syst. Sci.

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“…However, SAR imagery is also significantly affected by noise and is difficult for nonprofessionals to interpret. Previous studies [3][4][5][6][7][8] have also detected building changes by using multi-temporal very-high-resolution satellite data. The spectral information in these images is useful for separating buildings from vegetation.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Pang

Wang³

et al. 2014

Remote Sensing

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Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

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“…Therefore, rapid building mapping and damage assessments play a significant role in the speed of emergency responses and disaster reduction [1][2][3][4]. Since earthquakes cannot be predicted, such events must be managed using available satellite images.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Tang

Huang

et al. 2017

Remote Sensing

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Abstract:In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.

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Context-based mapping of damaged buildings from high-resolution optical satellite images

Cited by 54 publications

References 11 publications

A fuzzy decision making system for building damage map creation using high resolution satellite imagery

A fuzzy decision making system for building damage map creation using high resolution satellite imagery

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

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