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題名 基於深度學習評估台灣南部山崩動態:應用衛星圖像時間序列於山崩潛感圖和脆弱性評估
A Deep-Learning Approach to Evaluate Landslide Dynamics in Southern Taiwan: Application of Satellite Image Time Series (Sits) for Landslide Susceptibility Mapping and Vulnerability Assessments
作者 范噶色
貢獻者 地政系
關鍵詞 山崩; 地質災害; 社會脆弱性; 山崩潛感圖; 衛星圖像序列 (SITS); 遙感探測; 深度學習; 神經網絡; 永續發展目標 (SDG)
Landslides; Vulnerability; Susceptibility Mapping; Time Series; Remote Sensing; Deep Learning; Neural Networks; Sustainable Development Goals
日期 2025-04
上傳時間 14-Jul-2025 11:10:59 (UTC+8)
摘要 This project aims at providing data and higher-level information, including maps and spatiotemporal statistics, about the development and distribution of landslide hazards in Southern Taiwan with a focus on the Kaohsiung Special Municipality which has been chosen for its urban developments trends over the last decades on the one hand, and its complex mountain geomorphology and settings on the other. This morphodynamic results in high relief energy, high abundance of mass-wasting features and related dynamics and exposure of vulnerable settlements within the main mountain range. The investigation aims at providing quantitative information to assess and long-term monitor exposure and vulnerability of human settlements and constructions based on a semi-automatized approach using Deep Learning strategies and time series of openly accessible remote sensing data from Earth observing platforms. Results of this project (maps, foundational datasets, indicator metrics, statistics) are to be integrated into a webGIS dissemination platform that is in development within another approved project focusing on Land Use and Land Cover (LULC) change in Southern Taiwan, and all derived products are made publicly available for potential future regional development of planning strategies.
關聯 國家科學及技術委員會, NSTC112-2410-H004-142, 112.08-113.07
資料類型 report
dc.contributor 地政系
dc.creator (作者) 范噶色
dc.date (日期) 2025-04
dc.date.accessioned 14-Jul-2025 11:10:59 (UTC+8)-
dc.date.available 14-Jul-2025 11:10:59 (UTC+8)-
dc.date.issued (上傳時間) 14-Jul-2025 11:10:59 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158020-
dc.description.abstract (摘要) This project aims at providing data and higher-level information, including maps and spatiotemporal statistics, about the development and distribution of landslide hazards in Southern Taiwan with a focus on the Kaohsiung Special Municipality which has been chosen for its urban developments trends over the last decades on the one hand, and its complex mountain geomorphology and settings on the other. This morphodynamic results in high relief energy, high abundance of mass-wasting features and related dynamics and exposure of vulnerable settlements within the main mountain range. The investigation aims at providing quantitative information to assess and long-term monitor exposure and vulnerability of human settlements and constructions based on a semi-automatized approach using Deep Learning strategies and time series of openly accessible remote sensing data from Earth observing platforms. Results of this project (maps, foundational datasets, indicator metrics, statistics) are to be integrated into a webGIS dissemination platform that is in development within another approved project focusing on Land Use and Land Cover (LULC) change in Southern Taiwan, and all derived products are made publicly available for potential future regional development of planning strategies.
dc.format.extent 116 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) 國家科學及技術委員會, NSTC112-2410-H004-142, 112.08-113.07
dc.subject (關鍵詞) 山崩; 地質災害; 社會脆弱性; 山崩潛感圖; 衛星圖像序列 (SITS); 遙感探測; 深度學習; 神經網絡; 永續發展目標 (SDG)
dc.subject (關鍵詞) Landslides; Vulnerability; Susceptibility Mapping; Time Series; Remote Sensing; Deep Learning; Neural Networks; Sustainable Development Goals
dc.title (題名) 基於深度學習評估台灣南部山崩動態:應用衛星圖像時間序列於山崩潛感圖和脆弱性評估
dc.title (題名) A Deep-Learning Approach to Evaluate Landslide Dynamics in Southern Taiwan: Application of Satellite Image Time Series (Sits) for Landslide Susceptibility Mapping and Vulnerability Assessments
dc.type (資料類型) report