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題名 基於深度學習的自動化山崩地圖判讀研究：模型訓練與後處理方法
Automated Landslide Map Interpretation Based on Deep Learning: Model Training and Post-Processing Methods
作者 曾子玲
Zeng, Zi-Ling
貢獻者 林士淵
Lin, Shih-Yuan
曾子玲
Zeng, Zi-Ling
關鍵詞 山崩目錄
自動化圈繪
深度學習
後處理
日期 2023
上傳時間 2-Aug-2023 13:40:14 (UTC+8)
摘要 山崩這項天然災害於多山環境且降雨量高的臺灣影響甚大，而建置山崩目錄的工作能夠協助進行山崩災害的風險評估，並有效減災。然而，臺灣目前的山崩目錄建置工作缺乏一定的判釋標準，山崩的識別來自多方專家人工進行判釋，因此若能快速準確且合理地進行山崩判釋，即自動化圈繪山崩，相較於傳統手動方法，將能快速識別山崩區域，減少人工判讀的工作量，並能提供山崩目錄建置以及山崩災害管理一個更高效的方法。
本研究選擇南投縣作為透過深度學習的自動化山崩地圖判讀的研究區域。首先，選擇不同年份和月份的衛星影像，建立各年Unet山崩判釋模型，為模擬專家人工判釋過程，訓練模型使用了衛星影像的RBG值、遙測相關指標(如NDVI和NDWI)及地形因素(如TWI、坡度、數值高程模型)作為特徵。接著，進行模型的後處理，分為三階段進行調整：第一節段是設定二元門檻值，選擇產生較佳的F1-score的門檻值作為每個模型的相應門檻值；第二階段是使用集成模型，將各年份模型判釋成果結合起來生成最終的判釋成果；第三階段是僅選擇較佳精度的模型作為集成模型的參考，以提高整體判釋的準確性。綜合以上的後處理調整與方法，本研究完成穩定性高、可靠性高的自動化山崩地圖判釋模型。
本研究成果亦呈現模型對於山崩面積大小的精度影響，透過面積遮罩的方式探討模型的準確性及誤判性。在三階段的後處理調整及方法應用，模型於三年的山崩預測精度F1-score為58.61%、58.69%、59.84%，接近60%的穩定判釋能力。
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2. Azmoon B, Biniyaz A, Liu Z. Use of High-Resolution Multi-Temporal DEM Data for Landslide Detection. Geosciences. 2022;12(10):378.
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43. Martha TR, Kamala P, Jose J, Vinod Kumar K, Jai Sankar G. Identification of new Landslides from High Resolution Satellite Data Covering a Large Area Using Object-Based Change Detection Methods. Journal of the Indian Society of Remote Sensing. 2016;44(4):515-24. doi: 10.1007/s12524-015-0532-7.
44. Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology. 2010;116(1):24-36. doi: https://doi.org/10.1016/j.geomorph.2009.10.004.
45. Milletari F, Navab N, Ahmadi S-A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). 2016:565-71.
46. Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sensing of Environment. 2011;115(7):1743-57. doi: https://doi.org/10.1016/j.rse.2011.03.006.
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描述 碩士
國立政治大學
地政學系
110257027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110257027
資料類型 thesis
dc.contributor.advisor 林士淵zh_TW
dc.contributor.advisor Lin, Shih-Yuanen_US
dc.contributor.author (Authors) 曾子玲zh_TW
dc.contributor.author (Authors) Zeng, Zi-Lingen_US
dc.creator (作者) 曾子玲zh_TW
dc.creator (作者) Zeng, Zi-Lingen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 13:40:14 (UTC+8)-
dc.date.available 2-Aug-2023 13:40:14 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 13:40:14 (UTC+8)-
dc.identifier (Other Identifiers) G0110257027en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146465-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 110257027zh_TW
dc.description.abstract (摘要) 山崩這項天然災害於多山環境且降雨量高的臺灣影響甚大，而建置山崩目錄的工作能夠協助進行山崩災害的風險評估，並有效減災。然而，臺灣目前的山崩目錄建置工作缺乏一定的判釋標準，山崩的識別來自多方專家人工進行判釋，因此若能快速準確且合理地進行山崩判釋，即自動化圈繪山崩，相較於傳統手動方法，將能快速識別山崩區域，減少人工判讀的工作量，並能提供山崩目錄建置以及山崩災害管理一個更高效的方法。
本研究選擇南投縣作為透過深度學習的自動化山崩地圖判讀的研究區域。首先，選擇不同年份和月份的衛星影像，建立各年Unet山崩判釋模型，為模擬專家人工判釋過程，訓練模型使用了衛星影像的RBG值、遙測相關指標(如NDVI和NDWI)及地形因素(如TWI、坡度、數值高程模型)作為特徵。接著，進行模型的後處理，分為三階段進行調整：第一節段是設定二元門檻值，選擇產生較佳的F1-score的門檻值作為每個模型的相應門檻值；第二階段是使用集成模型，將各年份模型判釋成果結合起來生成最終的判釋成果；第三階段是僅選擇較佳精度的模型作為集成模型的參考，以提高整體判釋的準確性。綜合以上的後處理調整與方法，本研究完成穩定性高、可靠性高的自動化山崩地圖判釋模型。
本研究成果亦呈現模型對於山崩面積大小的精度影響，透過面積遮罩的方式探討模型的準確性及誤判性。在三階段的後處理調整及方法應用，模型於三年的山崩預測精度F1-score為58.61%、58.69%、59.84%，接近60%的穩定判釋能力。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究架構 5
第二章 文獻回顧 6
第一節 傳統遙測偵測山崩 6
第二節 遙測偵測山崩新發展 11
第三節 文獻回顧小結 24
第三章 研究方法 26
第一節 研究區域 26
第二節 研究資料及工具 27
第三節 研究設計與理論基礎 32
第四章 實驗成果與分析 43
第一節 深度學習模型精度評估 43
第二節 模型測試成果與後處理分析 47
第五章 基於實驗成果的近一步探究 56
第六章 結論 61
參考文獻 63		zh_TW
dc.format.extent 3701977 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110257027en_US
dc.subject (關鍵詞) 山崩目錄zh_TW
dc.subject (關鍵詞) 自動化圈繪zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 後處理zh_TW
dc.title (題名) 基於深度學習的自動化山崩地圖判讀研究：模型訓練與後處理方法zh_TW
dc.title (題名) Automated Landslide Map Interpretation Based on Deep Learning: Model Training and Post-Processing Methodsen_US
dc.type (資料類型) thesisen_US
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