{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:00:11Z","timestamp":1781020811940,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001301"],"award-info":[{"award-number":["42001301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["AB20159034"],"award-info":[{"award-number":["AB20159034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011785","name":"Guangxi Science and Technology Project","doi-asserted-by":"publisher","award":["42001301"],"award-info":[{"award-number":["42001301"]}],"id":[{"id":"10.13039\/501100011785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011785","name":"Guangxi Science and Technology Project","doi-asserted-by":"publisher","award":["AB20159034"],"award-info":[{"award-number":["AB20159034"]}],"id":[{"id":"10.13039\/501100011785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model\u2019s accuracy across diverse geographical areas and terrains.<\/jats:p>","DOI":"10.3390\/rs15133350","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3284-297X","authenticated-orcid":false,"given":"Yuze","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing 100028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Han","sequence":"additional","affiliation":[{"name":"State Grid Siji Location Service Co., Ltd., Beijing 102200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhua","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing 100028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8995-9389","authenticated-orcid":false,"given":"Yu","family":"Zang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing 100028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minlu","family":"Zhou","sequence":"additional","affiliation":[{"name":"GuangXi Communications Design Group Co., Ltd., Nanning 530012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"ref_1","unstructured":"Turner, A.K., and Schuster, R.L. (1996). Landslides: Investigation and Mitigation, National Academy Press. Transportation Research Board Special Report 247."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide Risk Assessment and Management: An Overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geomorph.2010.04.009","article-title":"Deciphering the Effect of Climate Change on Landslide Activity: A Review","volume":"124","author":"Crozier","year":"2010","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global Patterns of Loss of Life from Landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_5","unstructured":"Varnes, D.J. (1984). Landslide Hazard Zonation: A Review of Principles and Practice, UNESCO."},{"key":"ref_6","unstructured":"Guzzetti, F. (2006). Landslide Hazard and Risk Assessment. [PhD. Thesis, University of Bonn]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pardeshi, S.D., Autade, S.E., and Pardeshi, S.S. (2013). Landslide Hazard Assessment: Recent Trends and Techniques, Springer Plus.","DOI":"10.1186\/2193-1801-2-523"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1139\/t95-063","article-title":"A model for the runout analysis of rapid flow slides, debris flows, and avalanches","volume":"32","author":"Hungr","year":"1995","journal-title":"Can. Geotech. J."},{"key":"ref_10","unstructured":"Baecher, G.B., and Christian, J.T. (2003). Reliability and Statistics in Geotechnical Engineering, Wiley."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1080\/13658810600661508","article-title":"GIS-based multicriteria decision analysis: A survey of the literature","volume":"20","author":"Malczewski","year":"2006","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ado, M., Wang, R.-Y., Lv, G.-A., and Jiao, L. (2022). Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sens., 14.","DOI":"10.3390\/rs14133029"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, H., Li, S., Ruan, W., Yao, J., Liu, Y., and Zhang, L. (2023). Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models. Remote Sens., 15.","DOI":"10.3390\/rs15071886"},{"key":"ref_14","first-page":"1897","article-title":"Landslide susceptibility mapping using machine learning algorithms and multi-source remote sensing data","volume":"17","author":"Chen","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_15","first-page":"111853","article-title":"Landslide susceptibility mapping using multi-source remote sensing data and an ensemble machine learning algorithm","volume":"246","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"1059","article-title":"A comparison of machine learning algorithms for regional landslide susceptibility mapping","volume":"17","author":"Alvioli","year":"2020","journal-title":"Landslides"},{"key":"ref_17","first-page":"725","article-title":"Landslide susceptibility mapping using deep learning-based convolutional neural networks with high-resolution satellite imagery","volume":"11","author":"Hong","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_18","first-page":"2336","article-title":"Landslide susceptibility mapping using deep convolutional neural network with multi-source remote sensing data","volume":"11","author":"Maji","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s10712-020-09609-1","article-title":"Remote Sensing for Assessing Landslides and Associated Hazards","volume":"41","author":"Lissak","year":"2020","journal-title":"Surv. Geophys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","article-title":"Landslide detection, monitoring and prediction with remote-sensing techniques","volume":"4","author":"Casagli","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_21","first-page":"1379","article-title":"Landslide identification using machine learning","volume":"17","author":"Li","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_22","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_23","first-page":"2829","article-title":"Landslide susceptibility mapping using XGBoost machine learning algorithm","volume":"11","author":"Althuwaynee","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_24","first-page":"1788","article-title":"Landslide susceptibility assessment using XGBoost machine learning model: A case study of Uttarakhand state in India","volume":"35","author":"Pradhan","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_25","first-page":"104352","article-title":"A novel hybrid machine learning model based on XGBoost and MARS for landslide susceptibility assessment","volume":"187","author":"Rahmati","year":"2020","journal-title":"Catena"},{"key":"ref_26","first-page":"3413","article-title":"Landslide susceptibility mapping using an improved XGboost algorithm: A case study in the Kii Peninsula, Japan","volume":"12","author":"Zhang","year":"2020","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Machine Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"726","DOI":"10.4236\/ijg.2016.75056","article-title":"Multi-resolution landslide susceptibility analysis using a DEM and random forest","volume":"7","author":"Paudel","year":"2016","journal-title":"Int. J. Geosci."},{"key":"ref_29","first-page":"112","article-title":"Time and Spacial Predicting of Geological Hazards Occurrence","volume":"S1","author":"Xu","year":"2000","journal-title":"J. Mt. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_31","first-page":"1809","article-title":"Mapping landslide susceptibility based on deep belief network","volume":"45","author":"Chen","year":"2020","journal-title":"Geomatics Inf. Sci. Wuhan Univ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1080\/13658816.2013.869821","article-title":"An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping","volume":"28","author":"Feizizadeh","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.geomorph.2005.12.003","article-title":"Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)","volume":"76","author":"Vanwalleghem","year":"2006","journal-title":"Geomorphology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.geomorph.2010.02.017","article-title":"Analysis of landslide inventories for accurate prediction of debris-flow source areas","volume":"119","author":"Blahut","year":"2010","journal-title":"Geomorphology"},{"key":"ref_36","first-page":"175","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2012","journal-title":"Landslides"},{"key":"ref_37","unstructured":"Chen, T., and He, T. (2014, January 8\u201313). Higgs boson discovery with boosted trees. Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning 2015, Montreal, QC, Canada."},{"key":"ref_38","unstructured":"James, G.M., Witten, D., Hastie, T., and Tibshirani, R. (2018). An Introduction to Statistical Learning, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Molnar, C. (2019). Interpretable Machine Learning, Lulu Press.","DOI":"10.21105\/joss.00786"},{"key":"ref_40","first-page":"102713","article-title":"A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping","volume":"108","author":"Lv","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","first-page":"102508","article-title":"A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping","volume":"104","author":"He","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7881","DOI":"10.1109\/JSTARS.2021.3101203","article-title":"Recognition and mapping of landslide using a fully convolutional densenet and influencing factors","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3350\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:59Z","timestamp":1760126639000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3350"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133350"],"URL":"https:\/\/doi.org\/10.3390\/rs15133350","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}