A Comprehensive Analysis of Building Damage in the 12 January 2010 Mw7 Haiti Earthquake Using High-Resolution Satelliteand Aerial Imagery

Corbane, Christina; Saito, Keiko; Dell'Oro, Luca; Bjørgo, Einar; Gill, Stuart; Piard, Boby Emmanuel; Huyck, Charles; Kemper, Thomas; Lemoine, Guido; Spence, Robin; Shankar, Ravi; Senegas, Olivier; Ghesquiere, Francis; Lallemant, David; Evans, Galen; Gartley, Ross A.; Toro, Joaquin; Ghosh, Shubharoop; Svekla, Walter; Adams, Beverley J.; Eguchi, Ronald T.

doi:10.14358/pers.77.10.0997

Cited by 85 publications

(38 citation statements)

References 10 publications

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“…We set IBCC hyper-parameters to α Since the Ushahidi data set contains only reports of emergencies, and does not contain reports stating that no emergency is taking place, we cannot learn the length-scale l from this data, and must rely on background knowledge. We therefore select another dataset from the Haiti 2010 Earthquake, which has gold standard labels, namely the building damage assessment provided by UNOSAT [2]. We expect this data to have a similar length-scale because the underlying cause of both the building damages and medical emergencies was an earthquake affecting built-up areas where people were present.…”

Section: Haiti Earthquake Text Messagesmentioning

confidence: 99%

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

Simpson

Reece

Roberts

2017

Machine Learning and Knowledge Discovery in Databases

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Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to applications such as situation awareness in disaster zones and mapping the spread of diseases. Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap". Annotating unstructured data using crowdsourcing or automated classifiers produces individual classifications at sparse locations that typically contain many errors. We propose a novel Bayesian approach that models the relevance, error rates and bias of each information source, enabling us to learn a spatial Gaussian Process classifier by aggregating data from multiple sources with varying reliability and relevance. Our method does not require gold-labelled data and can make predictions at any location in an area of interest given only sparse observations. We show empirically that our approach can handle noisy and biased data sources, and that simultaneously inferring reliability and transferring information between neighbouring reports leads to more accurate predictions. We demonstrate our method on two real-world problems from disaster response, showing how our approach reduces the amount of crowdsourced data required and can be used to generate valuable heatmap visualisations from SMS messages and satellite images.

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Section: Haiti Earthquake Text Messagesmentioning

confidence: 99%

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

Simpson

Reece

Roberts

2017

Machine Learning and Knowledge Discovery in Databases

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“…Most limitations arise due to data availability and misinterpretations (Kerle, 2010). Furthermore, patterns of damage are more likely to be accurately mapped, whereas the differentiated delineation of the damage state for individual structures appears less feasible (Corbane et al, 2011a). Furthermore, patterns of damage are more likely to be accurately mapped, whereas the differentiated delineation of the damage state for individual structures appears less feasible (Corbane et al, 2011a).…”

Section: Postevent Capabilities For Damage Assessment and Recovery Momentioning

confidence: 99%

The Capabilities of Earth Observation to Contribute along the Risk Cycle

Taubenböck

Geiß

Wieland

et al. 2014

Earthquake Hazard, Risk and Disasters

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“…Remote sensing technology is an important way to obtain information in the early stage of earthquake relief because of its objective and efficient access to a wide range of disaster information, which can provide information to support earthquake 10 damage assessment and emergency rescue. There are many works in the literature on this topic that consider both optical and radar data (Voigt et al, 2007;Ehrlich et al, 2009;Corbane et al, 2011). Notably, with the continuous development of satellite technology, the number of satellites operating in orbit and sensors are increasing, and a large amount of remote sensing data can be acquired after a disaster.…”

Section: Introductionmentioning

confidence: 99%

Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

Zhang

Jiang

2019

Preprint

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Abstract. After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, accurate post-earthquake assessment results are needed to manage recovery and reconstruction and estimate economic losses. In this paper, for quantification and precision purposes, information on earthquake-induced building damage is extracted using multi-source remote sensing images collected after an earthquake. The multi-source remote sensing data include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Features that describe texture, colour, and geometry are included in our analysis. The feature analysis is carried out according to the rough set theory to further determine the feature parameters. A logistic regression model (LRM) is employed to find the optimal fitting function to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. In our experiment, old Beichuan County, China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Through comparison with a ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potentials of different features for application in the field of damage detection.

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A Comprehensive Analysis of Building Damage in the 12 January 2010 Mw7 Haiti Earthquake Using High-Resolution Satelliteand Aerial Imagery

Cited by 85 publications

References 10 publications

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources

The Capabilities of Earth Observation to Contribute along the Risk Cycle

Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

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