{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:36:45Z","timestamp":1780774605637,"version":"3.54.1"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["CATENA"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1016\/j.catena.2020.104851","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T18:23:39Z","timestamp":1597775019000},"page":"104851","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":207,"special_numbering":"C","title":["Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region"],"prefix":"10.1016","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2653-8920","authenticated-orcid":false,"given":"Yaning","family":"Yi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanchang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.catena.2020.104851_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104225","article-title":"Landslide susceptibility hazard map in southwest Sweden using artificial neural network","volume":"183","author":"Abbaszadeh Shahri","year":"2019","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0010","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.scitotenv.2019.01.021","article-title":"A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran","volume":"660","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.catena.2020.104851_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment","volume":"188","author":"Bui","year":"2020","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0020","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Bui","year":"2016","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0025","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s10346-005-0021-0","article-title":"Landslide hazard and risk mapping at catchment scale in the Arno River basin","volume":"2","author":"Catani","year":"2005","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0030","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.catena.2020.104851_b0035","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s10346-010-0202-3","article-title":"Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model","volume":"7","author":"Chauhan","year":"2010","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0040","first-page":"1","article-title":"Landslide susceptibility assessment using different slope units based on the evidential belief function model","author":"Chen","year":"2019","journal-title":"Geocarto International"},{"key":"10.1016\/j.catena.2020.104851_b0045","first-page":"1389","article-title":"Probabilistic prediction models for landslide hazard mapping","volume":"65","author":"Chung","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0050","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1023\/B:NHAZ.0000007172.62651.2b","article-title":"Validation of spatial prediction models for landslide hazard mapping","volume":"30","author":"Chung","year":"2003","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.catena.2020.104851_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104179","article-title":"Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics","volume":"183","author":"Costache","year":"2019","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124808","article-title":"Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning","volume":"585","author":"Costache","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.catena.2020.104851_b0065","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s11069-009-9392-1","article-title":"The Wenchuan Earthquake (May 12, 2008), Sichuan Province, China, and resulting geohazards","volume":"56","author":"Cui","year":"2009","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.catena.2020.104851_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104451","article-title":"A spatially explicit deep learning neural network model for the prediction of landslide susceptibility","volume":"188","author":"Dao","year":"2020","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0075","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.catena.2020.104851_b0080","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s11069-019-03659-4","article-title":"Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China","volume":"97","author":"Dou","year":"2019","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.catena.2020.104851_b0085","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.137320","article-title":"Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning","volume":"720","author":"Dou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.catena.2020.104851_b0095","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11069-013-0907-4","article-title":"Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011","volume":"71","author":"Dowling","year":"2014","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.catena.2020.104851_b0100","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.geomorph.2004.09.025","article-title":"Artificial Neural Networks applied to landslide susceptibility assessment","volume":"66","author":"Ermini","year":"2005","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0105","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s10346-018-0960-x","article-title":"Coseismic landslides triggered by the 8th August 2017 Ms 7.0 Jiuzhaigou earthquake (Sichuan, China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification","volume":"15","author":"Fan","year":"2018","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0110","doi-asserted-by":"crossref","DOI":"10.1029\/2018RG000626","article-title":"Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts","author":"Fan","year":"2019","journal-title":"Rev. Geophys."},{"key":"10.1016\/j.catena.2020.104851_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2020.104470","article-title":"Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping","volume":"139","author":"Fang","year":"2020","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.catena.2020.104851_b0120","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0125","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.geomorph.2005.06.002","article-title":"Probabilistic landslide hazard assessment at the basin scale","volume":"72","author":"Guzzetti","year":"2005","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0130","article-title":"Completeness index for earthquake-induced landslide inventories","volume":"105331","author":"Hakan","year":"2019","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.329","article-title":"Landslide spatial modelling using novel bivariate statistical based Na\u00efve Bayes, RBF Classifier, and RBF Network machine learning algorithms","volume":"663","author":"He","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.catena.2020.104851_b0140","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R., 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580."},{"key":"10.1016\/j.catena.2020.104851_b0145","unstructured":"Ioffe, S., Szegedy, C., 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift, 32nd International Conference on Machine Learning (ICML). International Machine Learning Society (IMLS), Lile, France, pp. 448-456."},{"key":"10.1016\/j.catena.2020.104851_b0150","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.geomorph.2008.03.003","article-title":"GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region","volume":"101","author":"Kamp","year":"2008","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0155","doi-asserted-by":"crossref","first-page":"aac8353","DOI":"10.1126\/science.aac8353","article-title":"Geomorphic and geologic controls of geohazards induced by Nepal's 2015 Gorkha earthquake","volume":"351","author":"Kargel","year":"2016","journal-title":"Science"},{"key":"10.1016\/j.catena.2020.104851_b0160","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.cageo.2012.11.003","article-title":"Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal","volume":"52","author":"Kayastha","year":"2013","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.catena.2020.104851_b0165","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.catena.2020.104851_b0170","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10346-016-0771-x","article-title":"A modified frequency ratio method for landslide susceptibility assessment","volume":"14","author":"Li","year":"2017","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0175","doi-asserted-by":"crossref","first-page":"36274","DOI":"10.1109\/ACCESS.2019.2903127","article-title":"Deep learning-based classification methods for remote sensing images in urban built-up areas","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.catena.2020.104851_b0180","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.5194\/nhess-17-1411-2017","article-title":"Landslide susceptibility mapping on a global scale using the method of logistic regression","volume":"17","author":"Lin","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.catena.2020.104851_b0185","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"14","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0190","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate object localization in remote sensing images based on convolutional neural networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0195","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: a meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0200","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","article-title":"Landslide susceptibility assessment using SVM machine learning algorithm","volume":"123","author":"Marjanovi\u0107","year":"2011","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0205","article-title":"Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance","volume":"103225","author":"Merghadi","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.catena.2020.104851_b0210","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s10346-011-0305-5","article-title":"GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model","volume":"9","author":"Neuh\u00e4user","year":"2012","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2020.104458","article-title":"Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area","volume":"188","author":"Nhu","year":"2020","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0220","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0225","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.enggeo.2013.02.009","article-title":"Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area","volume":"158","author":"Papadopoulou-Vrynioti","year":"2013","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0230","doi-asserted-by":"crossref","first-page":"501","DOI":"10.5194\/esurf-3-501-2015","article-title":"Spatial distributions of earthquake-induced landslides and hillslope preconditioning in the northwest South Island","volume":"3","author":"Parker","year":"2015","journal-title":"New Zealand. Earth Surf. Dynam."},{"key":"10.1016\/j.catena.2020.104851_b0235","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0240","first-page":"1","article-title":"A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers","author":"Pham","year":"2018","journal-title":"Geocarto International"},{"key":"10.1016\/j.catena.2020.104851_b0245","doi-asserted-by":"crossref","first-page":"32727","DOI":"10.1109\/ACCESS.2020.2973415","article-title":"Convolutional neural network\u2014optimized moth flame algorithm for shallow landslide susceptible analysis","volume":"8","author":"Pham","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.catena.2020.104851_b0250","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.catena.2020.104851_b0255","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/19475705.2010.498151","article-title":"Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area","volume":"1","author":"Pradhan","year":"2010","journal-title":"Geomat. Nat. Hazard Risk"},{"key":"10.1016\/j.catena.2020.104851_b0260","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.catena.2020.104851_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2020","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0270","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.geomorph.2017.04.039","article-title":"Characterization and quantification of path dependency in landslide susceptibility","volume":"292","author":"Samia","year":"2017","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0275","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s10346-016-0739-x","article-title":"Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory","volume":"14","author":"Samia","year":"2017","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0280","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1007\/s10346-018-1024-y","article-title":"Implementing landslide path dependency in landslide susceptibility modelling","volume":"15","author":"Samia","year":"2018","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0285","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.catena.2019.03.017","article-title":"Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution","volume":"178","author":"Shirzadi","year":"2019","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0290","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.geomorph.2016.03.015","article-title":"Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps","volume":"262","author":"Steger","year":"2016","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0295","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1002\/2017JF004236","article-title":"Presentation and analysis of a worldwide database of earthquake-induced landslide inventories","volume":"122","author":"Tanyas","year":"2017","journal-title":"J. Geophys. Res. F: Earth Surf."},{"key":"10.1016\/j.catena.2020.104851_b0300","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s12583-018-0869-2","article-title":"Inventory and Spatial Distribution of Landslides Triggered by the 8th August 2017 MW 6.5 Jiuzhaigou Earthquake","volume":"30","author":"Tian","year":"2019","journal-title":"China. Journal of Earth Science"},{"key":"10.1016\/j.catena.2020.104851_b0305","doi-asserted-by":"crossref","DOI":"10.3390\/f10090743","article-title":"New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed","volume":"10","author":"Tien Bui","year":"2019","journal-title":"Forests"},{"key":"10.1016\/j.catena.2020.104851_b0310","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Economic Geography"},{"key":"10.1016\/j.catena.2020.104851_b0315","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview","volume":"102","author":"van Westen","year":"2008","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0320","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.scitotenv.2019.02.263","article-title":"Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China","volume":"666","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.catena.2020.104851_b0325","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s11629-017-4761-9","article-title":"Seismogenic fault and topography control on the spatial patterns of landslides triggered by the 2017 Jiuzhaigou earthquake","volume":"15","author":"Wu","year":"2018","journal-title":"J. Mount. Sci."},{"key":"10.1016\/j.catena.2020.104851_b0330","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s10346-015-0624-z","article-title":"Global research trends in landslides during 1991\u20132014: a bibliometric analysis","volume":"12","author":"Wu","year":"2015","journal-title":"Landslides"},{"key":"10.1016\/j.catena.2020.104851_b0335","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.geomorph.2015.07.002","article-title":"Database and spatial distribution of landslides triggered by the Lushan, China Mw 6.6 earthquake of 20 April 2013","volume":"248","author":"Xu","year":"2015","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0340","series-title":"IEEE International Geoscience and Remote Sensing Symposium","first-page":"9650","article-title":"Landslide Susceptibility Mapping Using Logistic Regression Model Based On Information Value for the Region Along China-Thailand Railway from Saraburi To Sikhio","author":"Xu","year":"2019"},{"key":"10.1016\/j.catena.2020.104851_b0345","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.enggeo.2010.09.009","article-title":"Landslide susceptibility mapping in Injae, Korea, using a decision tree","volume":"116","author":"Yeon","year":"2010","journal-title":"Eng. Geol."},{"key":"10.1016\/j.catena.2020.104851_b0350","series-title":"IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium","first-page":"9318","article-title":"Comparison of Different Machine Learning Models For Landslide Susceptibility Mapping","author":"Yi","year":"2019"},{"key":"10.1016\/j.catena.2020.104851_b0355","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.5194\/nhess-19-1973-2019","article-title":"GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province China","volume":"19","author":"Yi","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.catena.2020.104851_b0360","doi-asserted-by":"crossref","DOI":"10.3390\/rs11151774","article-title":"Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network","volume":"11","author":"Yi","year":"2019","journal-title":"Remote Sensing"},{"issue":"2","key":"10.1016\/j.catena.2020.104851_b0365","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s12040-013-0281-3","article-title":"An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ","volume":"122","author":"Yilmaz","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"10.1016\/j.catena.2020.104851_b0370","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.catena.2020.104851_b0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104188","article-title":"A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods","volume":"183","author":"Zhu","year":"2019","journal-title":"CATENA"},{"key":"10.1016\/j.catena.2020.104851_b0380","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.geomorph.2014.02.003","article-title":"An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic","volume":"214","author":"Zhu","year":"2014","journal-title":"Geomorphology"},{"key":"10.1016\/j.catena.2020.104851_b0385","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: a comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."}],"container-title":["CATENA"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S034181622030401X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S034181622030401X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T05:47:54Z","timestamp":1759729674000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S034181622030401X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":77,"alternative-id":["S034181622030401X"],"URL":"https:\/\/doi.org\/10.1016\/j.catena.2020.104851","relation":{},"ISSN":["0341-8162"],"issn-type":[{"value":"0341-8162","type":"print"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region","name":"articletitle","label":"Article Title"},{"value":"CATENA","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.catena.2020.104851","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"104851"}}