[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125873-en":3,"doc-seo-125873-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125873,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery - Article","Landslides driven by disturbed slope equilibrium threaten landscapes, infrastructure, and human life, often triggered by intense precipitation, seismic activity, or volcanic events. Effective disaster management requires detailed knowledge of landslide spatiotemporal patterns, dimensions, and morphology. Prior remote sensing methods frequently rely on either optical or synthetic aperture radar data, limiting accuracy. This study introduces ML-LaDeCORsat in Google Earth Engine, combining Sentinel-1, Palsar-2, and Sentinel-2 imagery with selected spectral indices for machine-learning classification and localization.","remote sensing  \nArticle  \nDetecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery  \nStefan Peters 1, *, Jixue Liu 1, Gunnar Keppel 1, Anna Wendleder 2 and Peiliang Xu 3  \nCitation: Peters, S.; Liu, J.; Keppel, G.; Wendleder, A.; Xu, P. Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery. Remote Sens. 2024, 16, 1722. [https://](https://)[ ](https://)[doi.org/10.3390/rs16101722](doi.org/10.3390/rs16101722)  \nAcademic Editors: Valerio Tramutoli, Francesco Marchese, Nicola Genzano and Carolina Filizzola  \nReceived: 9 April 2024  \nRevised: 7 May 2024  \nAccepted: 10 May 2024  \nPublished: 13 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Unit of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Mawson Lakes, SA 5095, Australia; [jixue.liu@unisa.edu.au](jixue.liu@unisa.edu.au) (J.L.); [gunnar.keppel@unisa.edu.au](gunnar.keppel@unisa.edu.au) (G.K.)  \n2 German Remote Sensing Data Center, German Aerospace Center (DLR), 82234 Weßling, Germany;  \nanna.wendleder@dlr.de  \n3 Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan; [pxu@rcep.dpri.kyoto-u.ac.jp](pxu@rcep.dpri.kyoto-u.ac.jp)  \n* Correspondence: [stefan.peters@unisa.edu.au](stefan.peters@unisa.edu.au); Tel.: +61-8-830-25245  \nAbstract: Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatiotemporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE) . The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes) . Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92% . MLLaDeCORsat can be adapted to other landslide events (GEE script is provided) . Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.  \nKeywords: landslide detection; satellite remote sensing; machine learning; classification; Google Earth Engine; transfer learning  \n1. Introduction—Context and Related Work  \nLandslides occur when a slope equilibrium is disturbed, causing downslope movement of soil, rock, and organic materials under the effects of gravit","cbCaigysP3DlZFhj","https://ap.wps.com/l/cbCaigysP3DlZFhj","pdf",9611752,2,1,29,"English","en",105,"# Introduction—Context and Related Work\n## Earthquakes and Coseismic Landslides","[{\"question\":\"What problem does ML-LaDeCORsat address?\",\"answer\":\"It addresses accurate and timely detection and localization of coseismic landslides by combining optical and radar satellite information within Google Earth Engine.\"},{\"question\":\"Which satellite datasets and features are used?\",\"answer\":\"The approach integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery and uses relevant spectral indices and suitable bands for classification.\"},{\"question\":\"How is the model evaluated and what classifiers are supported?\",\"answer\":\"The study uses a robust training and validation strategy and compares five classifiers: CART, Random Forest, GTB, SVM, and Naive Bayes, reporting 87–92% detection accuracy with a GTB-based model.\"}]","Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery - 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