DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Cha, Meeyoung | - |
dc.contributor.advisor | 차미영 | - |
dc.contributor.author | Kim, Danu | - |
dc.date.accessioned | 2023-06-26T19:31:43Z | - |
dc.date.available | 2023-06-26T19:31:43Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032960&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309578 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전산학부, 2023.2,[iv, 24 p. :] | - |
dc.description.abstract | The increasing frequency and severity of water-related disasters such as floods, tornadoes, hurricanes, and tsunamis in low- and middle-income countries exemplify the uneven effects of global climate change. The vulnerability of high-risk societies to natural disasters has continued to increase. Local damage assessments must be timely, exhaustive, and accurate to develop an effective and efficient adaptation strategy. We propose a novel deep-learning-based solution that uses pair of pre- and post-disaster satellite images to identify the water-related disaster-affected region. The model extracts features of pre- and post-disaster images and uses them to predict damage in the pair. We demonstrate that the model can successfully identify local destruction using less granular and less complex ground-truth data than previous segmentation models. When tested with various water-related disasters, our detection model reported an accuracy of 85.9% in spotting areas with damaged buildings. It also achieved a reliable performance in the case study on Hurricane Iota, showing its ability to be applied to real-disaster situations. Our deep learning-based damage assessment model can help direct resources to areas most vulnerable to climate disasters, reducing their impacts while promoting adaptive capacities for climate-resilient development in the most vulnerable regions. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Computer vision▼aMachine learning▼aDaytime satellite imagery▼aDamage detection▼aNatural disaster▼aDisaster response | - |
dc.subject | 컴퓨터 비젼▼a기계학습▼a주간 위성 영상▼a피해 탐지▼a자연 재해▼a재난 대응 | - |
dc.title | Water-related disaster assessment using computer vision and satellite imagery | - |
dc.title.alternative | 위성영상과 딥러닝을 활용한 건물 피해 탐지 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 김단우 | - |
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