Damaged Building Detection using Multiple Object Tracking and Decision Tree from Aerial Videos for Disaster Response

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dc.contributor.authorFujita, Shono
dc.contributor.authorHatayama, Michinori
dc.date.accessioned2023-12-26T18:38:01Z
dc.date.available2023-12-26T18:38:01Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.259
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other3e93ec22-3461-4740-8645-dc16fa91d8b0
dc.identifier.urihttps://hdl.handle.net/10125/106638
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDisaster Information, Resilience, for Emergency and Crisis Technologies
dc.subjectaerial video
dc.subjectdamaged building
dc.subjectdeep learning
dc.subjectdisaster response
dc.subjectearthquake
dc.titleDamaged Building Detection using Multiple Object Tracking and Decision Tree from Aerial Videos for Disaster Response
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractInformation about damaged buildings is crucial in disaster response owing to the risk they pose, including property damage and loss of human lives. However, it is difficult to capture this information rapidly during disaster. This study developed an automatic model to detect buildings damaged by earthquakes from aerial videos. It is composed of multiple object tracking model of buildings, classification model of damage, and decision tree model to output final estimation by each track. This system considers; (1) detection of damaged and collapsed buildings such as pancake collapse of wooden buildings and significant roof damage, (2) input of time-series information to determine the extent of building damage, (3) less annotation labor to train datasets, and (4) effective usage of decision tree nodes for disaster response. The obtained results indicated that the average recall of three classes was 47.9%, average precision was 48.4%, and average F-measure was 45.7%.
dcterms.extent10 pages
prism.startingpage2086

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