Leveraging YOLOv5 Object Detection on Satellite Imagery: A Comprehensive Building Damage Detection and Severity Assessment System for PostDisaster Environments

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dc.contributor.advisorDr. Shahnewaz Siddique
dc.contributor.authorAbdullah Hel Asif
dc.contributor.authorShafin Ahmed Zidan
dc.contributor.id1911331642
dc.contributor.id1912891042
dc.date.accessioned2025
dc.date.accessioned2025-07-15T05:53:31Z
dc.date.available2025-07-15T05:53:31Z
dc.date.issued2023
dc.description.abstractLeveraging YOLOv5 Object Detection on Satellite Imagery: A Comprehensive Building Damage Detection and Severity Assessment System for Post- Disaster Environments Natural disasters are a plague that has haunted life on Earth from the beginning of time. Whenever a natural disaster occurs first responders and aid workers always have to put their lives on the line to save the ones in need. The basis of our work is the creation of a system that can detect and analyze the severity of damage on post-disaster environments caused by natural events. Our Deep Learning based system is able to recognize damaged buildings and surrounding areas caused due to earthquakes, floods, etc. from satellite images and simultaneously assign a damage severity rating to it. We have made the use of the YOLOv5 model based on PyTorch as the backbone of our system. Our project will allow the first respondents and aid workers in question to efficiently distribute their resources and provide help based on degree of severity of damages in the event of a natural disaster.
dc.description.degreeUndergraduate
dc.identifier.cd600000177
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1263
dc.language.isoen
dc.publisherNorth South Univesity
dc.rights©NSU Library
dc.titleLeveraging YOLOv5 Object Detection on Satellite Imagery: A Comprehensive Building Damage Detection and Severity Assessment System for PostDisaster Environments
oaire.citation.endPage35
oaire.citation.startPage1
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