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Geomatics, Natural Hazards and Risk Volume 12, 2021 - Issue 1
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Articles

Multi-Regional landslide detection using combined unsupervised and supervised machine learning

Faraz S. Tehrania Deltares, Unit Geo-Engineering, Delft, The NetherlandsCorrespondence[email protected]
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Giorgio Santinellia Deltares, Unit Geo-Engineering, Delft, The NetherlandsView further author information
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Meylin Herrera Herrerab Formerly Geomatics Program, Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, The NetherlandsView further author information
Pages 1015-1038 | Received 26 Nov 2020, Accepted 27 Mar 2021, Published online: 19 Apr 2021

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Sartsin Phakdimek, Daisuke Komori & Thapthai Chaithong. (2023) Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree. International Journal of Remote Sensing 44:11, pages 3572-3606.
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Omid Ghorbanzadeh, Khalil Gholamnia & Pedram Ghamisi. (2022) The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images. Big Earth Data 0:0, pages 1-25.
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Yue Liu, Peihua Xu, Chen Cao, Bo Shan, Kuanxing Zhu, Qiuyang Ma, Zongshuo Zhang & Han Yin. (2021) A comparative evaluation of machine learning algorithms and an improved optimal model for landslide susceptibility: a case study. Geomatics, Natural Hazards and Risk 12:1, pages 1973-2001.
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Articles from other publishers (9)

Alexandra Jarna Ganerød, Erin Lindsay, Ola Fredin, Tor-Andre Myrvoll, Steinar Nordal & Jan Ketil Rød. (2023) Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape. Remote Sensing 15:4, pages 895.
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Qinghua Zhang, Zhu Liang, Wei Liu, Weiping Peng, Houzan Huang, Shouwen Zhang, Lingwei Chen, Kaihua Jiang & Lixing Liu. (2022) Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests. Sustainability 14:24, pages 16692.
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Sreelakshmi S.Vinod Chandra S. S.E. Shaji. (2022) Landslide identification using machine learning techniques: Review, motivation, and future prospects. Earth Science Informatics 15:4, pages 2063-2090.
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Sylvain Fiolleau, Denis Jongmans, Grégory Bièvre, Guillaume Chambon, Olivier Michel & Laurent Borgniet. (2022) Study of clay degradation in an earthslide combining OBIA and roughness analysis of UAV‐based optical images. Earth Surface Processes and Landforms 47:15, pages 3465-3480.
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Faraz S. Tehrani, Michele Calvello, Zhongqiang Liu, Limin Zhang & Suzanne Lacasse. (2022) Machine learning and landslide studies: recent advances and applications. Natural Hazards 114:2, pages 1197-1245.
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Hui Gao, Li He, Zheng-wei He & Wen-qian Bai. (2022) Early landslide mapping with slope units division and multi-scale object-based image analysis — A case study in the Xianshui River basin of Sichuan, China. Journal of Mountain Science 19:6, pages 1618-1632.
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Erin Lindsay, Regula Frauenfelder, Denise Rüther, Lorenzo Nava, Lena Rubensdotter, James Strout & Steinar Nordal. (2022) Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape. Remote Sensing 14:10, pages 2301.
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Omid Ghorbanzadeh, Yonghao Xu, Pedram Ghamisi, Michael Kopp & David Kreil. (2022) Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection. IEEE Transactions on Geoscience and Remote Sensing 60, pages 1-17.
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Lutfiye Kusak, Fatma Bunyan Unel, Aydın Alptekin, Mehmet Ozgur Celik & Murat Yakar. (2021) Apriori association rule and K -means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping . Open Geosciences 13:1, pages 1226-1244.
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