{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T03:06:21Z","timestamp":1768273581274,"version":"3.49.0"},"reference-count":86,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The University of South Australia, Unit of Science, Technology, Engineering and Mathematics (STEM)","award":["230314-001821"],"award-info":[{"award-number":["230314-001821"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 spatio-temporal 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\u201392%. ML-LaDeCORsat 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.<\/jats:p>","DOI":"10.3390\/rs16101722","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:33:03Z","timestamp":1715589183000},"page":"1722","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3604-4625","authenticated-orcid":false,"given":"Stefan","family":"Peters","sequence":"first","affiliation":[{"name":"Unit of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Mawson Lakes, SA 5095, Australia"}]},{"given":"Jixue","family":"Liu","sequence":"additional","affiliation":[{"name":"Unit of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Mawson Lakes, SA 5095, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7092-6149","authenticated-orcid":false,"given":"Gunnar","family":"Keppel","sequence":"additional","affiliation":[{"name":"Unit of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Mawson Lakes, SA 5095, Australia"}]},{"given":"Anna","family":"Wendleder","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, German Aerospace Center (DLR), 82234 We\u00dfling, Germany"}]},{"given":"Peiliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto 611-0011, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Highland, L., and Bobrowsky, P.T. (2008). The Landslide Handbook: A Guide to Understanding Landslides.","DOI":"10.3133\/cir1325"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1130\/0016-7606(1984)95<406:LCBE>2.0.CO;2","article-title":"Landslides Caused by Earthquakes","volume":"95","author":"Keefer","year":"1984","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_3","unstructured":"Ritchie, H., and Roser, M. (2023, July 09). Natural Disasters. Available online: https:\/\/ourworldindata.org\/natural-disasters."},{"key":"ref_4","unstructured":"Wallemacq, P., Below, R., and McClean, D. (2023, September 17). Economic Losses, Poverty & Disasters: 1998\u20132017. United Nations Office for Disaster Risk Reduction. Available online: https:\/\/www.preventionweb.net\/files\/61119_credeconomiclosses.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Quigley, M., and Duffy, B. (2020). Effects of Earthquakes on Flood Hazards: A Case Study from Christchurch, New Zealand. Geosciences, 10.","DOI":"10.3390\/geosciences10030114"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1007\/s10797-022-09747-9","article-title":"The Fiscal Costs of Earthquakes in Japan","volume":"30","author":"Noy","year":"2022","journal-title":"Int. Tax Public Financ."},{"key":"ref_7","first-page":"20","article-title":"Cruden, Dm, Varnes, Dj, 1996, Landslide Types and Processes, Transportation Research Board, Us National Academy of Sciences, Special Report, 247: 36\u201375","volume":"24","author":"Cruden","year":"1993","journal-title":"Landslides Eng. Pract."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"640043","DOI":"10.3389\/feart.2021.640043","article-title":"Data-Driven Landslide Nowcasting at the Global Scale","volume":"9","author":"Stanley","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ruggeri, P., Fruzzetti, V.M., Ferretti, A., and Scarpelli, G. (2020). Seismic and Rainfall Induced Displacements of an Existing Landslide: Findings from the Continuous Monitoring. Geosciences, 10.","DOI":"10.3390\/geosciences10030090"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40645-018-0169-6","article-title":"Landslides Triggered by an Earthquake and Heavy Rainfalls at Aso Volcano, Japan, Detected by Uas and Sfm-Mvs Photogrammetry","volume":"5","author":"Saito","year":"2018","journal-title":"Prog. Earth Planet. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"993975","DOI":"10.3389\/feart.2022.993975","article-title":"Assessment of Earthquake-Triggered Landslide Susceptibility Considering Coseismic Ground Deformation","volume":"10","author":"Zhao","year":"2023","journal-title":"Front. Earth Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.gsf.2020.09.004","article-title":"GIS-Based Landslide Susceptibility Modeling: A Comparison between Fuzzy Multi-Criteria and Machine Learning Algorithms","volume":"12","author":"Ali","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1038\/s41598-023-28096-z","article-title":"Comparison of Earthquake-Induced Shallow Landslide Susceptibility Assessment Based on Two-Category LR and KDE-MLR","volume":"13","author":"Fan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives","volume":"100","author":"Gamba","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_15","unstructured":"Yao, T., Green, D., Michael, K., and Davies, D. (October, January 27). Using Nasa Lance near Real-Time Products for Disaster Risk Reduction. Proceedings of the FOSS4G 2021 (Free and Open Source Software for Geospatial), Online."},{"key":"ref_16","unstructured":"NASA (2023, September 21). Near Real Time (Nrt) Data from Esa Sentinel Satellites, Available online: https:\/\/appliedsciences.nasa.gov\/join-mission\/publications-resources\/near-real-time-nrt-data-esa-sentinel-satellites."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shafapourtehrany, M., Batur, M., Shabani, F., Pradhan, B., Kalantar, B., and \u00d6zener, H. (2023). A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment. Remote Sens., 15.","DOI":"10.3390\/rs15071939"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and Alos\/Palsar-2 Sar Datasets for Mapping Active Landslides Along the Jinsha River Corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"753","DOI":"10.5194\/nhess-22-753-2022","article-title":"Generating Landslide Density Heatmaps for Rapid Detection Using Open-Access Satellite Radar Data in Google Earth Engine","volume":"22","author":"Handwerger","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.5194\/isprs-archives-XLIII-B3-2022-1181-2022","article-title":"Application of Sar Time-Series and Deep Learning for Estimating Landslide Occurrence Time","volume":"43","author":"Wang","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","unstructured":"Jensen, J.R. (2009). Remote Sensing of the Environment: An Earth Resource Perspective 2\/E, Pearson Education."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fitzgerald, D.L., Peters, S., Guerin, G.R., McGrath, A., and Keppel, G. (2023). Quantifying Dieback in a Vulnerable Population of Eucalyptus Macrorhyncha Using Remote Sensing. Land, 12.","DOI":"10.3390\/land12071271"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.5194\/isprs-annals-IV-2-W7-145-2019","article-title":"Aggregating Cloud-Free Sentinel-2 Images with Google Earth Engine","volume":"4","author":"Schmitt","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"673137","DOI":"10.3389\/feart.2021.673137","article-title":"Evaluation of Remote Mapping Techniques for Earthquake-Triggered Landslide Inventories in an Urban Subarctic Environment: A Case Study of the 2018 Anchorage, Alaska Earthquake","volume":"9","author":"Martinez","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_25","first-page":"1","article-title":"Remote Sensing Approaches and Related Techniques to Map and Study Landslides","volume":"2","author":"Ray","year":"2020","journal-title":"Landslides-Investig. Monit"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"964753","DOI":"10.3389\/feart.2022.964753","article-title":"Application of Remote Sensing and Gis in Earthquake-Triggered Landslides","volume":"10","author":"Xu","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_27","unstructured":"Byrraju, S.V. (2019). Landslide Detection Using Remote Sensing Methods A Review of Current Techniques, University of South Carolina."},{"key":"ref_28","unstructured":"Kader, M.A., and Jahan, I. (2019, January 12\u201314). A Review of the Application of Remote Sensing Technologies in Earthquake Disaster Management: Potentialities and Challenges. Proceedings of the International Conference on Disaster Risk Management, Dhaka, Bangladesh."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2625","DOI":"10.5194\/nhess-23-2625-2023","article-title":"Semi-Automatic Mapping of Shallow Landslides Using Free Sentinel-2 and Google Earth Engine","volume":"23","author":"Notti","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111235","DOI":"10.1016\/j.rse.2019.111235","article-title":"Landslide Mapping from Multi-Sensor Data through Improved Change Detection-Based Markov Random Field","volume":"231","author":"Lu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Rahimzad, M., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Homayouni, S., Blaschke, T., Lim, S., and Ghamisi, P. (2021). Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224698"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI-D-17-0022.1","article-title":"Automated Satellite-Based Landslide Identification Product for Nepal","volume":"23","author":"Fayne","year":"2019","journal-title":"Earth Interact."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40677-021-00189-9","article-title":"Adapting Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) Algorithms to Map Rainfall-Triggered Landslides in Western Cameroon Highlands (Central-Africa)","volume":"8","author":"Tchindjang","year":"2021","journal-title":"Geoenviron. Disasters"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1553\/giscience2021_01_s39","article-title":"Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery","volume":"9","author":"Piralilou","year":"2021","journal-title":"GI_Forum"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1007\/s10346-019-01207-6","article-title":"Characteristics of Landslides Triggered by the 2018 Hokkaido Eastern Iburi Earthquake, Northern Japan","volume":"16","author":"Zhang","year":"2019","journal-title":"Landslides"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s10661-021-09119-6","article-title":"Exploitation of Optical and SAR Amplitude Imagery for Landslide Identification: A Case Study from Sikkim, Northeast India","volume":"193","author":"Sivasankar","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_37","first-page":"1","article-title":"Using Google Earth Engine to Monitor Co-Seismic Landslide Recovery after the 2008 Wenchuan Earthquake","volume":"2020","author":"Yang","year":"2020","journal-title":"Earth Surf. Dyn. Discuss."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide Detection Using Deep Learning and Object-Based Image Analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y. (2019). Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 data. Remote Sens., 11.","DOI":"10.3390\/rs11202351"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"679","DOI":"10.5194\/isprs-archives-XLIII-B3-2021-679-2021","article-title":"Landslide Detection in Central America Using the Differential Bare Soil Index","volume":"43","author":"Ariza","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12665-018-7516-1","article-title":"Analysis of Satellite-Derived Landslide at Central Nepal from 2011 to 2016","volume":"77","author":"Yu","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Subiyantoro, A., Westen, C.J.V., Bout, B.V.D., Yuniawan, R.A., and Mulyana, A.R. (2022, January 24\u201325). Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi. Proceedings of the 2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Yogyakarta, Indonesia.","DOI":"10.1109\/ICARES56907.2022.9993507"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.5194\/nhess-21-1495-2021","article-title":"HazMapper: A Global Open-Source Natural Hazard Mapping Application in Google Earth Engine","volume":"21","author":"Scheip","year":"2021","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Singh, P., Maurya, V., and Dwivedi, R. (2021, January 11\u201316). Pixel Based Landslide Identification Using Landsat 8 and Gee. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553358"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lindsay, E., Frauenfelder, R., R\u00fcther, D., Nava, L., Rubensdotter, L., Strout, J., and Nordal, S. (2022). Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape. Remote Sens., 14.","DOI":"10.3390\/rs14102301"},{"key":"ref_46","first-page":"1","article-title":"Rapid Landslide Identification Using Synthetic Aperture Radar Amplitude Change Detection on the Google Earth Engine","volume":"2020","author":"Handwerger","year":"2020","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1007\/s10346-021-01735-0","article-title":"Co-Seismic Landslide Detection after M 7.4 Earthquake on June 23, 2020, in Oaxaca, Mexico, Based on Rapid Mapping Method Using High and Medium Resolution Synthetic Aperture Radar (Sar) Images","volume":"18","author":"Hernandez","year":"2021","journal-title":"Landslides"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s40623-019-1046-2","article-title":"Detection and Interpretation of Local Surface Deformation from the 2018 Hokkaido Eastern Iburi Earthquake Using Alos-2 Sar Data","volume":"71","author":"Fujiwara","year":"2019","journal-title":"Earth Planets Space"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ju, Y., Xu, Q., Jin, S., Li, W., Su, Y., Dong, X., and Guo, Q. (2022). Loess Landslide Detection Using Object Detection Algorithms in Northwest China. Remote Sens., 14.","DOI":"10.3390\/rs14051182"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yu, Z., Chang, R., and Chen, Z. (2022). Automatic Detection Method for Loess Landslides Based on Gee and an Improved Yolox Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14184599"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhao, L., Liu, J., Peters, S., Li, J., Oliver, S., and Mueller, N. (2022). Investigating the Impact of Using Ir Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight Cnn Model. Remote Sens., 14.","DOI":"10.3390\/rs14133047"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gomes, V.C., Queiroz, G.R., and Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12081253"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mutanga, O., and Kumar, L. (2019). Google Earth Engine Applications. Remote. Sens., 11.","DOI":"10.3390\/rs11050591"},{"key":"ref_54","first-page":"100907","article-title":"What Is Going on within Google Earth Engine? A Systematic Review and Meta-Analysis","volume":"29","author":"Zema","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.5194\/esurf-8-1053-2020","article-title":"Geraldine (Google Earth Engine Supraglacial Debris Input Detector): A New Tool for Identifying and Monitoring Supraglacial Landslide Inputs","volume":"8","author":"Smith","year":"2020","journal-title":"Earth Surf. Dyn."},{"key":"ref_56","first-page":"102829","article-title":"World-Wide Insar Sensitivity Index for Landslide Deformation Tracking","volume":"111","author":"Bogaard","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhang, Q., Singh, V.P., Wang, G., Zhao, J., Shen, Z., and Sun, S. (2022). A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai\u2013Tibetan Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14184662"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ilmy, H.F., Darminto, M.R., and Widodo, A. (2020, January 26). Application of Machine Learning on Google Earth Engine to Produce Landslide Susceptibility Mapping (Case Study: Pacitan). Proceedings of the IOP Conference Series: Earth and Environmental Science, Online.","DOI":"10.1088\/1755-1315\/731\/1\/012028"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ado, M., Amitab, K., Maji, A.K., Jasi\u0144ska, E., Gono, R., Leonowicz, Z., and Jasi\u0144ski, M. (2022). Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sens., 14.","DOI":"10.3390\/rs14133029"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, K., Wang, M., He, Q., Jiang, Z., Zhu, W., and Qiao, N. (2022). Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14225795"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Khan, M.M., Ghaffar, B., Shahzad, R., Khan, M.R., Shah, M., Amin, A.H., Eldin, S.M., Naqvi, N.A., and Ali, R. (2022). Atmospheric Anomalies Associated with the 2021 M W 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites. Sustainability, 14.","DOI":"10.3390\/su142214782"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1007\/s10346-019-01187-7","article-title":"Coseismic Landslides Triggered by the 2018 Hokkaido, Japan (M W 6.6), Earthquake: Spatial Distribution, Controlling Factors, and Possible Failure Mechanism","volume":"16","author":"Wang","year":"2019","journal-title":"Landslides"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"108419","DOI":"10.1016\/j.geomorph.2022.108419","article-title":"Evaluation of Factors Controlling the Spatial and Size Distributions of Landslides, 2021 Nippes Earthquake, Haiti","volume":"415","author":"Zhao","year":"2022","journal-title":"Geomorphology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"106504","DOI":"10.1016\/j.enggeo.2021.106504","article-title":"The World\u2019s Second-Largest, Recorded Landslide Event: Lessons Learnt from the Landslides Triggered during and after the 2018 Mw 7.5 Papua New Guinea Earthquake","volume":"297","author":"Hill","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1007\/s10346-022-01869-9","article-title":"An Open Dataset for Landslides Triggered by the 2016 Mw 7.8 Kaik\u014dura Earthquake, New Zealand","volume":"19","author":"Fadel","year":"2022","journal-title":"Landslides"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s40623-019-1036-4","article-title":"Seismic Potential around the 2018 Hokkaido Eastern Iburi Earthquake Assessed Considering the Viscoelastic Relaxation","volume":"71","author":"Ohtani","year":"2019","journal-title":"Earth Planets Space"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1007\/s10346-018-1092-z","article-title":"Landslides by the 2018 Hokkaido Iburi-Tobu Earthquake on September 6","volume":"15","author":"Yamagishi","year":"2018","journal-title":"Landslides"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Martinez, S.N., Allstadt, K.E., Slaughter, S.L., Schmitt, R.G., Collins, E., Schaefer, L.N., and Ellison, S. (2021). Landslides Triggered by the August 14, 2021, Magnitude 7.2 Nippes, Haiti, Earthquake.","DOI":"10.3133\/ofr20211112"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e2019EA000966","DOI":"10.1029\/2019EA000966","article-title":"The 2018 Mw 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event with Deep-Seated Reverse Faulting","volume":"7","author":"Wang","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.5194\/gmd-15-7933-2022","article-title":"Bayesian Atmospheric Correction over Land: Sentinel-2\/Msi and Landsat 8\/Oli","volume":"15","author":"Yin","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_71","unstructured":"Le Toan, T., Beaudoin, A., Riom, J., and Guyon, D. (1991, January 3\u20136). Relating forest parameters to SAR data. Proceedings of the IGARSS\u201991 Remote Sensing: Global Monitoring for Earth Management, Espoo, Finland."},{"key":"ref_72","unstructured":"Suri, M. (2019). Global Solar Atlas 2.0 Technical Report, World Bank."},{"key":"ref_73","unstructured":"Google (2023, September 02). The Global Precipitation Measurement Mission (Gpm). Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/NASA_GPM_L3_IMERG_V06."},{"key":"ref_74","unstructured":"Google (2023, February 15). Supervised Classification\u2014The Classifier Package Handles Supervised Classification by Traditional Ml Algorithms Running in Earth Engine. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/classification."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.asr.2021.10.024","article-title":"Assessing the Performance of Machine Learning Algorithms for Soil Salinity Mapping in Google Earth Engine Platform Using Sentinel-2a and Landsat-8 Oli Data","volume":"69","author":"Aksoy","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_76","unstructured":"Al-Amri, S.S., Kalyankar, N.V., and Khamitkar, S.D. (2010). A Comparative Study of Removal Noise from Remote Sensing Image. arXiv."},{"key":"ref_77","first-page":"306","article-title":"A Balanced Accuracy Function for Epistasis Modeling in Imbalanced Datasets Using Multifactor Dimensionality Reduction","volume":"31","author":"Velez","year":"2007","journal-title":"Genet. Epidemiol. Off. Publ. Int. Genet. Epidemiol. Soc."},{"key":"ref_78","first-page":"1","article-title":"Wood Species Identification Based on an Ensemble of Deep Convolution Neural Networks","volume":"66","author":"He","year":"2021","journal-title":"Wood Res."},{"key":"ref_79","first-page":"101152","article-title":"Learning Class-Specific Spectral Patterns to Improve Deep Learning-Based Scene-Level Fire Smoke Detection from Multi-Spectral Satellite Imagery","volume":"34","author":"Zhao","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chang, S., Deng, Y., Zhang, Y., Wang, R., Qiu, J., Wang, W., Zhao, Q., and Liu, D. (2022). An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding Sar Based on Digital Beamforming and Blind Source Separation. Remote Sens., 14.","DOI":"10.3390\/rs14153585"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"113924","DOI":"10.1016\/j.rse.2023.113924","article-title":"Transfer Learning in Environmental Remote Sensing","volume":"301","author":"Ma","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Luo, L., Wang, X., Guo, H., Lasaponara, R., Shi, P., Bachagha, N., Li, L., Yao, Y., Masini, N., and Chen, F. (2018). Google Earth as a Powerful Tool for Archaeological and Cultural Heritage Applications: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10101558"},{"key":"ref_83","unstructured":"Masson-Delmotte, V., Zhai, P., Pirani, S., Connors, C., P\u00e9an, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M., and Scheel Monteiro, P.M. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"11543","DOI":"10.1073\/pnas.1808979115","article-title":"Poleward Migration of the Destructive Effects of Tropical Cyclones during the 20th Century","volume":"115","author":"Altman","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"eabm8438","DOI":"10.1126\/sciadv.abm8438","article-title":"A Globally Consistent Local-Scale Assessment of Future Tropical Cyclone Risk","volume":"8","author":"Bloemendaal","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_86","unstructured":"Knutson, T.R., Chung, M.V., Vecchi, G., Sun, J., Hsieh, T.-L., and Smith, A.J. (2021). Climate Change Is Probably Increasing the Intensity of Tropical Cyclones. Crit. Issues Clim. Chang. Sci. Sci. Brief Rev., 4570334."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1722\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:41:34Z","timestamp":1760107294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":86,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101722"],"URL":"https:\/\/doi.org\/10.3390\/rs16101722","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,13]]}}}