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dc.contributor.authorKim, Jooho
dc.contributor.authorKanumuri, Ruthvik
dc.contributor.authorHatzis, Joshua J.
dc.contributor.authorPark, Sunyoung
dc.contributor.authorCampbell, Patrick A.
dc.contributor.authorCalhoun, Kristin M.
dc.date.accessioned2026-08-20T21:09:15Z
dc.date.available2026-08-20T21:09:15Z
dc.date.copyright2026/06/08
dc.date.issued2026
dc.identifier.otherNHREFO.NHENG-2701.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314035
dc.description.abstractAbstractAccurate assessment of tornado impacts requires detailed information on building characteristics that influence vulnerability. This study investigates a convolutional neural network (CNN)-based approach to classify nonresidential buildings into ...Practical ApplicationsThis study shows how street-level imagery can be used to build a more detailed and up-to-date building inventory focused on commercial and industrial structure, which are often limited or generalized in national data sets such as ...
dc.publisherAmerican Society of Civil Engineers
dc.titleCNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments
dc.typeJournal Article
dc.journal.volume27
dc.journal.issue3
dc.journal.titleNatural Hazards Review
dc.identifier.doi10.1061/NHREFO.NHENG-2701
dc.journal.fristpage04026018-1
dc.journal.lastpage04026018-18
dc.page18
dc.treeNatural Hazards Review:;2026:;Volume ( 027 ):;issue: 003
dc.contenttypeFulltext


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