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

Date
2024-01-03
Authors
Fujita, Shono
Hatayama, Michinori
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2086
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Abstract
Information 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%.
Description
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Disaster Information, Resilience, for Emergency and Crisis Technologies, aerial video, damaged building, deep learning, disaster response, earthquake
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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