English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113873/144892 (79%)
Visitors : 51911993      Online Users : 556
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146465


    Title: 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
    Authors: 曾子玲
    Zeng, Zi-Ling
    Contributors: 林士淵
    Lin, Shih-Yuan
    曾子玲
    Zeng, Zi-Ling
    Keywords: 山崩目錄
    自動化圈繪
    深度學習
    後處理
    Date: 2023
    Issue Date: 2023-08-02 13:40:14 (UTC+8)
    Abstract: 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。
    Reference: 1. 林彥廷, 顏筱穎, 張乃軒, 林宏明, 韓仁毓, 楊國鑫, et al. 應用AI學習技術於坡地崩塌預測分析-以高雄市小林村為例. 土木水利. 2021;48(2):48-55. doi: 10.6653/MoCICHE.202104_48(2).0007.
    2. Azmoon B, Biniyaz A, Liu Z. Use of High-Resolution Multi-Temporal DEM Data for Landslide Detection. Geosciences. 2022;12(10):378.
    3. Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017;39(12):2481-95. doi: 10.1109/TPAMI.2016.2644615.
    4. Bajracharya B, Bajracharya S. LANDSLIDE MAPPING OF THE EVEREST REGION USING HIGH RESOLUTION SATELLITE IMAGES AND 3D VISUALIZATION. 2008.
    5. Bernat Gazibara S, Krkač M, Mihalić Arbanas S. Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia). Journal of Maps. 2019;15(2):773-9. doi: 10.1080/17445647.2019.1671906.
    6. Borghuis AM, Chang K, Lee HY. Comparison between automated and manual mapping of typhoon‐triggered landslides from SPOT‐5 imagery. International Journal of Remote Sensing. 2007;28(8):1843-56. doi: 10.1080/01431160600935638.
    7. Cascini L, Fornaro G, Peduto D. Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing. 2009;64(6):598-611. doi: https://doi.org/10.1016/j.isprsjprs.2009.05.003.
    8. Chen H, He Y, Zhang L, Yao S, Yang W, Fang Y, et al. A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images. International Journal of Digital Earth. 2023;16(1):552-77. doi: 10.1080/17538947.2023.2177359.
    9. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille A. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. CoRR arXiv. 2014.
    10. Chen L-C, Papandreou G, Schroff F, Adam H. Rethinking Atrous Convolution for Semantic Image Segmentation. 2017.
    11. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer Vision – ECCV 2018. Cham: Springer International Publishing; 2018. p. 833-51.
    12. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(4):834-48. doi: 10.1109/TPAMI.2017.2699184.
    13. Cruden D, Varnes D. Landslides: Investigation and Mitigation. 1996.
    14. Czuchlewski K, Weissel J, Kim Y. Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan. Journal of Geophysical Research. 2003;108. doi: 10.1029/2003JF000037.
    15. Ferretti A, Prati C, Rocca F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR Interferometry. Geoscience and Remote Sensing, IEEE Transactions on. 2000;38:2202-12. doi: 10.1109/36.868878.
    16. Fiorucci F, Cardinali M, Carlà R, Rossi M, Mondini AC, Santurri L, et al. Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology. 2011;129(1):59-70. doi: https://doi.org/10.1016/j.geomorph.2011.01.013.
    17. Gabriel AK, Goldstein RM, Zebker HA. Mapping small elevation changes over large areas: Differential radar interferometry. Journal of Geophysical Research. 1989;94:9183-91.
    18. Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing. 2019;11(2):196.
    19. Ghorbanzadeh O, Crivellari A, Ghamisi P, Shahabi H, Blaschke T. A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan). Scientific Reports. 2021;11(1):14629. doi: 10.1038/s41598-021-94190-9.
    20. Ghorbanzadeh O, Shahabi H, Crivellari A, Homayouni S, Blaschke T, Ghamisi P. Landslide detection using deep learning and object-based image analysis. Landslides. 2022;19(4):929-39. doi: 10.1007/s10346-021-01843-x.
    21. Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV)2015. p. 1440-8.
    22. Girshick R, Donahue J, Darrell T, Malik J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition2014. p. 580-7.
    23. Guzzetti F, Manunta M, Ardizzone F, Pepe A, Cardinali M, Zeni G, et al. Analysis of Ground Deformation Detected Using the SBAS-DInSAR Technique in Umbria, Central Italy. Pure and Applied Geophysics. 2009;166(8):1425-59. doi: 10.1007/s00024-009-0491-4.
    24. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T. Landslide inventory maps: New tools for an old problem. Earth-Science Reviews. 2012;112(1):42-66. doi: https://doi.org/10.1016/j.earscirev.2012.02.001.
    25. Haeberlin Y, Turberg P, Retiere A, Senegas O, Parriaux A. VALIDATION OF SPOT-5 SATELLITE IMAGERY FOR GEOLOGICAL HAZARD IDENTIFICATION AND RISK ASSESSMENT FOR LANDSLIDES , MUD AND DEBRIS FLOWS IN MATAGALPA , NICARAGUA. 2004.
    26. He K, Zhang X, Ren S, Sun J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014;37. doi: 10.1109/TPAMI.2015.2389824.
    27. Iverson R. Iverson, R.M.: Landslide triggering by rain infiltration. Water Resour. Res. 36, 1897-1910. Water Resources Research - WATER RESOUR RES. 2000;36. doi: 10.1029/2000WR900090.
    28. Jaboyedoff M, Oppikofer T, Abellán A, Derron M-H, Loye A, Metzger R, et al. Use of LIDAR in landslide investigations: a review. Natural Hazards. 2012;61(1):5-28. doi: 10.1007/s11069-010-9634-2.
    29. Ji S, Yu D, Shen C, Li W, Xu Q. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides. 2020;17(6):1337-52. doi: 10.1007/s10346-020-01353-2.
    30. Kanungo DP, Arora MK, Sarkar S, Gupta RP. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology. 2006;85(3):347-66. doi: https://doi.org/10.1016/j.enggeo.2006.03.004.
    31. Lary DJ, Alavi AH, Gandomi AH, Walker AL. Machine learning in geosciences and remote sensing. Geoscience Frontiers. 2016;7(1):3-10. doi: https://doi.org/10.1016/j.gsf.2015.07.003.
    32. Lauknes TR, Piyush Shanker A, Dehls JF, Zebker HA, Henderson IHC, Larsen Y. Detailed rockslide mapping in northern Norway with small baseline and persistent scatterer interferometric SAR time series methods. Remote Sensing of Environment. 2010;114(9):2097-109. doi: https://doi.org/10.1016/j.rse.2010.04.015.
    33. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. doi: 10.1038/nature14539.
    34. Lei T, Zhang Y, Lv Z, Li S, Liu S, Nandi AK. Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters. 2019;16(6):982-6. doi: 10.1109/LGRS.2018.2889307.
    35. Li L, Qin Z, Zhang Q. Landslide Recognition Based on the Improved U-net. 2021 4th International Conference on Computer Science and Software Engineering (CSSE 2021). 2021.
    36. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature Pyramid Networks for Object Detection. 2016.
    37. Liu J-K, Wong C-C, Huang J-H, Yang M-J. LANDSLIDE-ENHANCEMENT IMAGES FOR THE STUDY OF TORRENTIAL-RAINFALL LANDSLIDES. 2002.
    38. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016. Cham: Springer International Publishing; 2016. p. 21-37.
    39. Lu P, Stumpf A, Kerle N, Casagli N. Object-Oriented Change Detection for Landslide Rapid Mapping. IEEE Geoscience and Remote Sensing Letters. 2011;8(4):701-5. doi: 10.1109/LGRS.2010.2101045.
    40. Ma Z, Mei G. Deep learning for geological hazards analysis: Data, models, applications, and opportunities. Earth-Science Reviews. 2021;223:103858. doi: https://doi.org/10.1016/j.earscirev.2021.103858.
    41. Marcelino EV, Formaggio AR, Maeda EE. Landslide inventory using image fusion techniques in Brazil. International Journal of Applied Earth Observation and Geoinformation. 2009;11(3):181-91. doi: https://doi.org/10.1016/j.jag.2009.01.003.
    42. Martha TR, Babu Govindharaj K, Vinod Kumar K. Damage and geological assessment of the 18 September 2011 Mw 6.9 earthquake in Sikkim, India using very high resolution satellite data. Geoscience Frontiers. 2015;6(6):793-805. doi: https://doi.org/10.1016/j.gsf.2013.12.011.
    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.
    47. Moosavi V, Talebi A, Shirmohammadi B. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology. 2014;204:646-56. doi: https://doi.org/10.1016/j.geomorph.2013.09.012.
    48. Nava L, Bhuyan K, Meena SR, Monserrat O, Catani F. Rapid Mapping of Landslides on SAR Data by Attention U-Net. Remote Sensing. 2022;14(6):1449.
    49. Park N-W, Chi K-H. Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model. International Journal of Remote Sensing - INT J REMOTE SENS. 2008;29:247-64. doi: 10.1080/01431160701227661.
    50. Prakash N, Manconi A, Loew S. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sensing. 2020;12(3):346.
    51. Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F. A review of statistically-based landslide susceptibility models. Earth-Science Reviews. 2018;180:60-91. doi: https://doi.org/10.1016/j.earscirev.2018.03.001.
    52. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015;39. doi: 10.1109/TPAMI.2016.2577031.
    53. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer International Publishing; 2015. p. 234-41.
    54. Santangelo M, Cardinali M, Rossi M, Mondini AC, Guzzetti F. Remote landslide mapping using a laser rangefinder binocular and GPS. Nat Hazards Earth Syst Sci. 2010;10(12):2539-46. doi: 10.5194/nhess-10-2539-2010.
    55. Schulz WH. Landslides mapped using LIDAR imagery, Seattle, Washington. 2004.
    56. Shahabi H, Rahimzad M, Ghorbanzadeh O, Piralilou ST, Blaschke T, Homayouni S, et al. Rapid Mapping of Landslides from Sentinel-2 Data Using Unsupervised Deep Learning. 2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)2022. p. 17-20.
    57. Shahabi H, Rahimzad M, Tavakkoli Piralilou S, Ghorbanzadeh O, Homayouni S, Blaschke T, et al. Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery. Remote Sensing. 2021 doi: 10.3390/rs13224698.
    58. Shaikh SH, Saeed K, Chaki N. Moving Object Detection Approaches, Challenges and Object Tracking. In: Shaikh SH, Saeed K, Chaki N, editors. Moving Object Detection Using Background Subtraction. Cham: Springer International Publishing; 2014. p. 5-14.
    59. Shi W, Zhang M, Ke H, Fang X, Zhan Z, Chen S. Landslide Recognition by Deep Convolutional Neural Network and Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 2021;59(6):4654-72. doi: 10.1109/TGRS.2020.3015826.
    60. Stumpf A, Kerle N. Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment. 2011;115(10):2564-77. doi: https://doi.org/10.1016/j.rse.2011.05.013.
    61. Tyagi A, Kamal Tiwari R, James N. A review on spatial, temporal and magnitude prediction of landslide hazard. Journal of Asian Earth Sciences: X. 2022;7:100099. doi: https://doi.org/10.1016/j.jaesx.2022.100099.
    62. Wang H, Zhang L, Yin K, Luo H, Li J. Landslide identification using machine learning. Geoscience Frontiers. 2021;12(1):351-64. doi: https://doi.org/10.1016/j.gsf.2020.02.012.
    63. Wolff Moine M, Puissant A, Malet JP. Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonnette basin (Alpes-de-Haute-Provence, France). Landslide Processes: From Geomorphological Mapping to Dynamic Modelling. 2009.
    64. Yang S, Wang Y, Wang P, Mu J, Jiao S, Zhao X, et al. Automatic Identification of Landslides Based on Deep Learning. Applied Sciences. 2022;12(16):8153.
    65. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid Scene Parsing Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017. p. 6230-9.
    66. Zhao ZQ, Zheng P, Xu ST, Wu X. Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems. 2019;30(11):3212-32. doi: 10.1109/TNNLS.2018.2876865.
    67. Zhiqiang W, Jun L. A review of object detection based on convolutional neural network. 2017 36th Chinese Control Conference (CCC)2017. p. 11104-9.
    68. Zou Z, Shi Z, Guo Y, Ye J. Object Detection in 20 Years: A Survey. 2019.
    Description: 碩士
    國立政治大學
    地政學系
    110257027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110257027
    Data Type: thesis
    Appears in Collections:[地政學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    702701.pdf3615KbAdobe PDF2130View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback