Detection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiers

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dc.contributor.authorZawadzka, Joanna
dc.contributor.authorTruckell, Ian
dc.contributor.authorKhouakhi, Abdou
dc.contributor.authorRivas Casado, Monica
dc.date.accessioned2021-10-15T13:44:11Z
dc.date.available2021-10-15T13:44:11Z
dc.date.issued2021-09-30
dc.description.abstractTimely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.en_UK
dc.identifier.citationZawadzka J, Truckell I, Khouakhi A, Rivas Casado M. (2021) Detection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiers. Remote Sensing, Volume 13, Issue 19, Article number 3913en_UK
dc.identifier.issn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs13193913
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/17174
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecturban flood damageen_UK
dc.subjectUAVen_UK
dc.subjectobject-oriented image analysisen_UK
dc.titleDetection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiersen_UK
dc.typeArticleen_UK

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