Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data

Ural, Serkan; Hussain, Ejaz; Kim, KyoHyouk; Fu, Chiung-Shiuan; Shan, Jie

doi:10.14358/pers.77.10.1011

Cited by 57 publications

(20 citation statements)

References 18 publications

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“…It is prone to being correctly classified for concrete buildings having very prominent collapse or damage structures with totally broken down roofs. Steel or wooden frame buildings with metal sheet roofs, where the building was physically collapsed but there was no visible deformation or textural change to its roof structure, would be hard to correctly classify [63], which would decrease the number of correctly classified collapsed buildings. The Kappa value for test A (37.7%) and test B (45.6%) also indicated that SqueezeNet performed better on test B to discriminate collapsed buildings, which could be partly caused by the difference in building structures in these two regions.…”

Section: Identifying Collapsed Buildings Using Cnnsmentioning

confidence: 99%

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Liu

Buchroithner

2018

Remote Sensing

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Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.

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Section: Identifying Collapsed Buildings Using Cnnsmentioning

confidence: 99%

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Liu

Buchroithner

2018

Remote Sensing

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“…More than 180 government buildings also collapsed, including 87% of key offices (DesRoches et al ., ). The majority of the buildings that collapsed were made of lightly reinforced concrete frames filled with concrete masonry (EERI, ; Fierro and Perry, ; Ural et al ., ) and, as a result, the bulk of the waste produced took the form of rubble derived from these materials. The nature of the rubble generated in Haiti had important implications for its clearance.…”

Section: Post‐disaster Rubble Clearance: Context and Haitian Experiencementioning

confidence: 99%

When Diverse Norms Meet Weak Plans: The Organizational Dynamics of Urban Rubble Clearance in Post‐Earthquake Haiti

Hooper¹

2018

Int J Urban Regional Res

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In the face of rapid global urbanization, cities are increasingly at risk from major disasters. While considerable attention has been given to other aspects of urban post‐disaster recovery, less has been dedicated to rubble clearance. This article examines the dynamics of rubble clearance through a study of how the diverse organizations responding to the 2010 Haiti earthquake addressed this issue in Port‐au‐Prince. It draws on interviews with 52 organizations spanning the range of organizational types engaged in recovery efforts. Building on an evaluation of rubble clearance plans developed for other seismically at‐risk cities, the results indicate that organizations in Port‐au‐Prince lacked a shared understanding of who should coordinate, and engage in, this vital activity. The diverse organizations involved in recovery brought widely diverging norms concerning rubble clearance to a context with weak plans in place for this task. The outcome was uncoordinated action and slow progress. The article draws on the literature on interorganizational norms to contextualize its findings from Port‐au‐Prince. It concludes by arguing for efforts to generate new, shared norms concerning who should lead urban rubble clearance and, in particular, for greater support for, and deference to, local plans around this issue. Such plans would enable governments to establish robust protocols to coordinate action on this increasingly important challenge for cities.

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“…Producing damage maps using experts after disasters in a manual manner proves our claim. To the best of our knowledge, in the damage detection application, the fuzzy-based decision making systems can be used for two main procedures including: (1) land use/cover classification [29,30] and (2) modeling the damage extent of buildings from the extracted features [2,13]. Ural et al (2011) employed a fuzzy classifier in order to map buildings and their rubble after the 2010 Haiti earthquake in a robust manner [30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Undefined values of variables are corrected using Equations (24) and (25). Finally, using Equations (15) to (20), costs of the new children are calculated and inserted into Equation (30).…”

Section: Stage 3: Decision Makingmentioning

confidence: 99%

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

Janalipour

Mohammadzadeh

2017

Remote Sensing

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Abstract:In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient.

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Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data

Cited by 57 publications

References 18 publications

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

When Diverse Norms Meet Weak Plans: The Organizational Dynamics of Urban Rubble Clearance in Post‐Earthquake Haiti

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

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