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Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models\u2019 best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly.<\/jats:p>","DOI":"10.3390\/rs15153901","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T06:38:48Z","timestamp":1691390328000},"page":"3901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost"],"prefix":"10.3390","volume":"15","author":[{"given":"Na","family":"Lin","sequence":"first","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2675-1622","authenticated-orcid":false,"given":"Di","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Shanshan","family":"Feng","sequence":"additional","affiliation":[{"name":"Qinghai Transportation Planning and Design Institute Co., Ltd., Xining 810000, China"}]},{"given":"Kai","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Libing","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Geomatics and Remote Sensing Center, Chongqing 401125, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-1256","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-8801","authenticated-orcid":false,"given":"Weile","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xiaoai","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Jianping","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Feifei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11417","DOI":"10.1109\/JSTARS.2021.3117975","article-title":"Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jenvman.2021.112067","article-title":"Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation","volume":"284","author":"Arabameri","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pang, D.D., Liu, G., He, J., Li, W.L., and Fu, R. (2022). Automatic Remote Sensing Identification of Co-Seismic Landslides Using Deep Learning Methods. Forests, 13.","DOI":"10.3390\/f13081213"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote Sensing of Landslides\u2014A Review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2019.03.013","article-title":"Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017","volume":"225","author":"Chen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"5235","article-title":"Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions","volume":"14","author":"Cai","year":"2021","journal-title":"IEEE J.-STARS"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1080\/17538947.2022.2062467","article-title":"Evaluation of neural network models for landslide susceptibility assessment","volume":"15","author":"Yi","year":"2022","journal-title":"Int J Digit Earth."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14547","DOI":"10.1080\/10106049.2022.2088863","article-title":"Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology","volume":"37","author":"Liu","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_9","first-page":"18","article-title":"Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping","volume":"9","author":"Wang","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.gsf.2020.02.012","article-title":"Landslide identification using machine learning","volume":"12","author":"Wang","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on landslide susceptibility mapping using support vector machines","volume":"165","author":"Huang","year":"2018","journal-title":"Catena."},{"key":"ref_12","first-page":"1","article-title":"Landslide susceptibility assessment using feature selection-based machine learning models","volume":"25","author":"Liu","year":"2021","journal-title":"Geomech. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jenvman.2021.114367","article-title":"Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Incheon, South Korea","volume":"305","author":"Hakim","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15","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":"ref_15","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Karimi-Sangchini, E., Pal, S.C., Saha, A., Chowdhuri, I., Lee, S., and Bui, D.T. (2020). Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12203389"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, S.H., Wang, Y.W., and Wu, G. (2022). Earthquake-Induced Landslide Susceptibility Assessment Using a Novel Model Based on Gradient Boosting Machine Learning and Class Balancing Methods. Remote Sens., 14.","DOI":"10.3390\/rs14235945"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jia, D., Yang, L., Gao, X., and Li, K. (2023). Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP). Remote Sens., 15.","DOI":"10.3390\/rs15092245"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Wu, W., and Liu, H. (2022). Exploring the Influencing Factors in Identifying Soil Texture Classes Using Multitemporal Landsat-8 and Sentinel-2 Data. Remote Sens., 14.","DOI":"10.2139\/ssrn.4181497"},{"key":"ref_19","first-page":"2623","article-title":"Optuna: A Next-generation Hyperparameter Optimization Framework","volume":"8","author":"Akiba","year":"2019","journal-title":"ACM"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep Residual Learning for Image Recognition: A Survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gu, H., Han, Y., Yang, Y., Li, H., Liu, Z., Soergel, U., Blaschke, T., and Cui, S. (2018). An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10040590"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1080\/15481603.2016.1273438","article-title":"Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractal net evolution approach","volume":"54","author":"Su","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_24","first-page":"3128","article-title":"A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces","volume":"7","author":"Zeng","year":"2014","journal-title":"IEEE J.-STARS"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, W., and Li, Y. (2020). Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree. Remote Sens., 12.","DOI":"10.3390\/rs12050783"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1007\/s11676-021-01420-x","article-title":"Pine wilt disease detection in high-resolution UAV images using object-oriented classification","volume":"33","author":"Sun","year":"2022","journal-title":"J. For. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Dragut","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_28","first-page":"146","article-title":"Sensitivity of multiresolution segmentation to spatial extent","volume":"81","author":"Dragut","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, R., Zhang, F., Xia, J., and Wu, C. (2022). Landslide Extraction Using Mask R-CNN with Background-Enhancement Method. Remote Sens., 14.","DOI":"10.3390\/rs14092206"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s10346-015-0671-5","article-title":"Landslide dam formation susceptibility analysis based on geomorphic features","volume":"13","author":"Chen","year":"2016","journal-title":"Landslides"},{"key":"ref_31","first-page":"12","article-title":"HADeenNet: A hierarchical-attention multi-scale deconvolution network for landslide detection","volume":"111","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/19475705.2023.2213807","article-title":"A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples","volume":"14","author":"Sun","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, R., Peng, J., Leng, Y., Lee, S., Panahi, M., Chen, W., and Zhao, X. (2021). Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility. Remote Sens., 13.","DOI":"10.3390\/rs13244966"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10346-017-0861-4","article-title":"The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: Geomorphological features and landslide distribution","volume":"15","author":"Rosi","year":"2018","journal-title":"Landslides"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rong, G., Alu, S., Li, K., Su, Y., Zhang, J., Zhang, Y., and Li, T. (2020). Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models\u2014A Case Study of Shuicheng County, China. Water, 12.","DOI":"10.3390\/w12113066"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1007\/s10346-022-01947-y","article-title":"Rapid prediction of landslide dam stability considering the missing data using XGBoost algorithm","volume":"19","author":"Shi","year":"2022","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7367","DOI":"10.1007\/s13369-022-06560-8","article-title":"Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)","volume":"47","author":"Kavzoglu","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/19475705.2021.1944330","article-title":"A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping","volume":"12","author":"Pham","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_41","first-page":"3149","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/19475705.2023.2190858","article-title":"Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China","volume":"14","author":"Liu","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"Bentejac","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liang, W., Luo, S., Zhao, G., and Wu, H. (2020). Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms. Mathematics, 8.","DOI":"10.3390\/math8050765"},{"key":"ref_45","first-page":"119","article-title":"A decision-theoretic generalization of online learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"JCSS"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.catena.2019.104396","article-title":"Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping","volume":"187","author":"Wu","year":"2020","journal-title":"Catena"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Clague, J.J., Jaafari, A., Chen, W., and Nguyen, H. (2020). Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17144933"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Wang, M., and Liu, K. (2023). Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu. Remote Sens., 15.","DOI":"10.3390\/rs15030798"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.jhydrol.2017.03.020","article-title":"A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping","volume":"548","author":"Naghibi","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_50","first-page":"4768","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.catena.2022.106379","article-title":"Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards","volume":"216","author":"Ekmekcioglu","year":"2022","journal-title":"Catena"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jenvman.2023.117357","article-title":"Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model","volume":"332","author":"Zhang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s10064-022-02708-w","article-title":"Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)","volume":"81","author":"Kavzoglu","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pradhan, B., Sameen, M.I., Al-Najjar, H.A.H., Sheng, D., Alamri, A.M., and Park, H.-J. (2021). A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides. Remote Sens., 13.","DOI":"10.3390\/rs13224521"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1007\/s10346-022-01923-6","article-title":"Metaheuristic-based support vector regression for landslide displacement prediction: A comparative study","volume":"19","author":"Ma","year":"2022","journal-title":"Landslides"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sestras, P., Bilaco, T., Rosca, S., Nas, S., Bondrea, M.V., Galgau, R., Veres, I., Salagean, T., Spalevic, V., and C\u00eempeanu, S.M. (2019). Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area. Sustainability, 11.","DOI":"10.3390\/su11051362"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hussain, M.A., Chen, Z., Zheng, Y., Shoaib, M., Shah, S.U., Ali, N., and Afzal, Z. (2022). Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique. Sensors, 22.","DOI":"10.3390\/s22093119"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ge, T., Tian, W., and Liou, Y.-A. (2019). Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China. Remote Sens., 11.","DOI":"10.3390\/rs11232801"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1177\/03091333221113660","article-title":"Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain","volume":"47","author":"Riaz","year":"2023","journal-title":"Prog Phys Geog."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lu, H., Ma, L., Fu, X., Liu, C., and Li, N. (2020). Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning. Remote Sens., 12.","DOI":"10.3390\/rs12050752"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3901\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:27:14Z","timestamp":1760128034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":60,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153901"],"URL":"https:\/\/doi.org\/10.3390\/rs15153901","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,7]]}}}