Attention For Damage Assessment

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dc.contributor.authorChowdhury, Tashnim
dc.contributor.authorRahnemoonfar, Maryam
dc.date.accessioned2021-08-06T16:56:57Z
dc.date.available2021-08-06T16:56:57Z
dc.date.issued2021
dc.descriptionTackling Climate Change with Machine Learning Workshop at ICML 2021en_US
dc.description.abstractDue to climate change the hurricanes are getting stronger and having longer impacts. To reduce the detrimental effects of these hurricanes faster and accurate assessments of damages are essential to the rescue teams. Like other computer vision techniques semantic segmentation can identify the damages and help in proper and prompt damage assessment. Current segmentation methods can be classified into attention and non-attention based methods. Existing non-attention based methods suffers from low accuracy and therefore attention based methods are becoming popular. Self-attention based methods can map the mutual relationship and dependencies among pixels of an image and thus improve semantic segmentation accuracy. In this paper, we present a self-attention semantic segmentation method on UAV imageries to assess the damages inflicted by a natural disaster. The proposed method outperforms four state-of-art segmentation methods both quantitatively and qualitatively with a mean IoU score of 84.03 %.en_US
dc.description.urihttps://www.climatechange.ai/papers/icml2021/37en_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepresentations (communicative events)en_US
dc.identifierdoi:10.13016/m2e3ki-oiig
dc.identifier.urihttp://hdl.handle.net/11603/22324
dc.language.isoenen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.subjecthurricanesen_US
dc.subjectsemantic segmentationen_US
dc.subjectdamage assessmenten_US
dc.subjectself-attention semantic segmentation method on UAV imageries
dc.subjectassessment of natural disaster damages
dc.titleAttention For Damage Assessmenten_US
dc.typeTexten_US

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