{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:05:42Z","timestamp":1771913142169,"version":"3.50.1"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"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":["42162025"],"award-info":[{"award-number":["42162025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019223","name":"Nanchang Institute for Microtechnology, Tianjin University","doi-asserted-by":"publisher","award":["YJSCX202305"],"award-info":[{"award-number":["YJSCX202305"]}],"id":[{"id":"10.13039\/501100019223","id-type":"DOI","asserted-by":"publisher"}]},{"name":"2022 Open fund of Hebei Center for Ecological and Environmental Geology Research","award":["JSYF-Z202201"],"award-info":[{"award-number":["JSYF-Z202201"]}]},{"name":"Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province","award":["PCMGH-2021-02"],"award-info":[{"award-number":["PCMGH-2021-02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The detection of landslide areas and surface characteristics is the prerequisite and basis of landslide hazard risk assessment. The traditional method relies mainly on manual field identification, and discrimination is based on the lack of unified quantitative standards. Thus, the use of neural networks for the quantitative identification and prediction of landslide surface deformation is explored. By constructing an integrated model based on YOLO X-CNN and Mask R-CNN, a deep learning-based feature detection method for landslide surface images is proposed. First, the method superimposes Unmanned Aerial Vehicle (UAV) oblique photography data (UOPD) and Internet heterosource image data (IHID) to construct a landslide surface image dataset and landslide surface deformation database. Second, an integrated model suitable for small- and medium-scale target detection and large-scale target edge extraction is constructed to automatically identify and extract landslide surface features and to achieve rapid detection of landslide surface features and accurate segmentation and deformation recognition of landslide areas. The results show that the detection accuracy for small rock targets is greater than 80% and that the speed is 57.04 FPS. The classification and mask segmentation accuracies of large slope targets are approximately 90%. A speed of 7.89 FPS can meet the needs of disaster emergency response; this provides a reference method for the accurate identification of landslide surface features.<\/jats:p>","DOI":"10.1007\/s44196-024-00655-w","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T12:02:03Z","timestamp":1728388923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Method for Identifying Landslide Surface Deformation via the Integrated YOLOX and Mask R-CNN Model"],"prefix":"10.1007","volume":"17","author":[{"given":"Chenghui","family":"Wan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2525-3632","authenticated-orcid":false,"given":"Jianjun","family":"Gan","sequence":"additional","affiliation":[]},{"given":"Anbang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Prabin","family":"Acharya","sequence":"additional","affiliation":[]},{"given":"Fenghui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wenjie","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Fangzhou","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"655_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/BF02590167","volume":"43","author":"DM Cruden","year":"1991","unstructured":"Cruden, D.M.: A simple definition of a landslide. Bull. Int. Assoc. Eng. Geol. 43, 27\u201329 (1991). https:\/\/doi.org\/10.1007\/BF02590167","journal-title":"Bull. Int. Assoc. Eng. Geol."},{"key":"655_CR2","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1007\/s00477-021-02165-z","volume":"36","author":"C Yong","year":"2022","unstructured":"Yong, C., Jinlong, D., Fei, G., Bin, T., Tao, Z., Hao, F., Li, W., Zhan, Q.: Review of landslide susceptibility assessment based on knowledge mapping. Stoch Environ. Res. Risk Assess. 36, 2399\u20132417 (2022). https:\/\/doi.org\/10.1007\/s00477-021-02165-z","journal-title":"Stoch Environ. Res. Risk Assess."},{"issue":"4","key":"655_CR3","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/rs9040314","volume":"9","author":"W Sun","year":"2017","unstructured":"Sun, W., Tian, Y., Mu, X., Zhai, J., Gao, P., Zhao, G.: Loess landslide inventory map based on GF-1 satellite imagery. Remote Sens. 9(4), 314 (2017). https:\/\/doi.org\/10.3390\/rs9040314","journal-title":"Remote Sens."},{"issue":"4","key":"655_CR4","doi-asserted-by":"publisher","first-page":"510","DOI":"10.5589\/m03-018","volume":"29","author":"J Barlow","year":"2003","unstructured":"Barlow, J., Martin, Y., Franklin, S.E.: Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British Columbia. Can. J. Remote. Sens. 29(4), 510\u2013517 (2003). https:\/\/doi.org\/10.5589\/m03-018","journal-title":"Can. J. Remote. Sens."},{"key":"655_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105150","volume":"114","author":"J Ma","year":"2022","unstructured":"Ma, J., Xia, D., Wang, Y., Niu, X., Jiang, S., Liu, Z., Guo, H.: A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction. Eng. Appl. Artif. Intell. 114, 105150 (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.105150","journal-title":"Eng. Appl. Artif. Intell."},{"key":"655_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.117357","volume":"332","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Ma, X., Zhang, J., Sun, D., Zhou, X., Mi, C., Wen, H.: Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. J. Environ. Manage. 332, 117357 (2023). https:\/\/doi.org\/10.1016\/j.jenvman.2023.117357","journal-title":"J. Environ. Manage."},{"issue":"7","key":"655_CR7","doi-asserted-by":"publisher","first-page":"26","DOI":"10.9781\/ijimai.2022.11.008","volume":"7","author":"H Zainab","year":"2021","unstructured":"Zainab, H., Moamin, A., Karrar, H., Seifedine, K., Mazin, A., Mohammed, N., Alaa, S., Jan, N.: Adaptive deep learning detection model for multifoggy images[J]. Int. J. Interact. Multimed. Artif. Intell. 7(7), 26\u201337 (2021). https:\/\/doi.org\/10.9781\/ijimai.2022.11.008","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"655_CR8","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1049\/ipr2.12365","volume":"16","author":"H Zainab","year":"2021","unstructured":"Zainab, H., Moamin, A.M., Karrar, H., Mazin, A., Mohammed, N., Ammar, A., Robertas, D.: Comprehensive review of machine learning (ML) in image defogging: taxonomy of concepts, scenes, feature extraction, and classification techniques[J]. IET Image Process 16, 289\u2013310 (2021). https:\/\/doi.org\/10.1049\/ipr2.12365","journal-title":"IET Image Process"},{"key":"655_CR9","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420 (2021). https:\/\/doi.org\/10.1007\/s42979-021-00815-1","journal-title":"SN Comput. Sci."},{"key":"655_CR10","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s11831-019-09344-w","volume":"27","author":"S Dargan","year":"2020","unstructured":"Dargan, S., Kumar, M., Ayyagari, M.R., Kumar, G.: A survey of deep learning and its applications: a new paradigm to machine learning. Arch. Comput. Methods Eng. 27, 1071\u20131092 (2020). https:\/\/doi.org\/10.1007\/s11831-019-09344-w","journal-title":"Arch. Comput. Methods Eng."},{"issue":"16","key":"655_CR11","doi-asserted-by":"publisher","first-page":"3928","DOI":"10.3390\/rs14163928","volume":"14","author":"R Fu","year":"2022","unstructured":"Fu, R., He, J., Liu, G., Li, W., Mao, J., He, M., Lin, Y.: Fast seismic landslide detection based on improved mask R-CNN. Remote Sens. 14(16), 3928 (2022). https:\/\/doi.org\/10.3390\/rs14163928","journal-title":"Remote Sens."},{"issue":"8","key":"655_CR12","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.1007\/s10346-021-01694-6","volume":"18","author":"Ch Li","year":"2021","unstructured":"Li, Ch., Li, J., Duan, P.: A small attentional YOLO model for landslide detection from satellite remote sensing images. Landslides 18(8), 2751\u20132765 (2021). https:\/\/doi.org\/10.1007\/s10346-021-01694-6","journal-title":"Landslides"},{"key":"655_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108254","volume":"116","author":"A Jaafari","year":"2022","unstructured":"Jaafari, A., Panahi, M., Mafi-Gholami, D., Rahmati, O., Shahabi, H., Shirzadi, A., Lee, S., Bui, D.T., Pradhan, B.: Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides. Appl. Soft Comput. 116, 108254 (2022). https:\/\/doi.org\/10.1016\/j.asoc.2021.108254","journal-title":"Appl. Soft Comput."},{"key":"655_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2020.104805","volume":"195","author":"BT Pham","year":"2020","unstructured":"Pham, B.T., Nguyen-Thoi, T., Qi, C., Phong, T.V., Dou, J., Ho, L.S., Le, H.V., Prakash, I.: Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 195, 104805 (2020). https:\/\/doi.org\/10.1016\/j.catena.2020.104805","journal-title":"CATENA"},{"key":"655_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.gexplo.2022.106962","volume":"236","author":"J Zhou","year":"2022","unstructured":"Zhou, J., Yu, W., Du, Y., Liu, X., Wang, Y.H., Xiong, G.L., Zhao, Z.Y., Pang, D.W., Shen, D.X., Weng, S.F., Liu, Z.C., Chen, D.: Provenance change and continental weathering of Late Permian bauxitic claystone in Guizhou Province, Southwest China. J. Geochem. Explor. 236, 106962 (2022). https:\/\/doi.org\/10.1016\/j.gexplo.2022.106962","journal-title":"J. Geochem. Explor."},{"issue":"3","key":"655_CR16","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/83.366477","volume":"4","author":"K Jensen","year":"1995","unstructured":"Jensen, K., Anastassiou, D.: Subpixel edge localization and the interpolation of still images. IEEE Trans. Image Process. 4(3), 285\u2013295 (1995). https:\/\/doi.org\/10.1109\/83.366477","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"655_CR17","doi-asserted-by":"publisher","first-page":"0255684","DOI":"10.1371\/journal.pone.0255684","volume":"16","author":"X Liu","year":"2021","unstructured":"Liu, X., Sang, X., Chang, J., Zheng, Y., Han, Y.: The water supply association analysis method in Shenzhen based on k-means clustering discretization and apriori algorithm. PLoS\u00a0One 16(8), 0255684 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0255684","journal-title":"PLoS\u00a0One"},{"key":"655_CR18","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","volume":"82","author":"T Diwan","year":"2023","unstructured":"Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed. Tools Appl. 82, 9243\u20139275 (2023). https:\/\/doi.org\/10.1007\/s11042-022-13644-y","journal-title":"Multimed. Tools Appl."},{"issue":"17","key":"655_CR19","doi-asserted-by":"publisher","first-page":"7596","DOI":"10.3390\/s23177596","volume":"23","author":"Q He","year":"2023","unstructured":"He, Q., Xu, A., Ye, Z., Zhou, W., Cai, T.: Object detection based on lightweight YOLOX for autonomous driving. Sensors 23(17), 7596 (2023). https:\/\/doi.org\/10.3390\/s23177596","journal-title":"Sensors"},{"key":"655_CR20","doi-asserted-by":"publisher","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun J. Yolox: Exceeding yolo series in 2021. arXiv preprint. http:\/\/arxiv.org\/2107.08430 (2021). https:\/\/doi.org\/10.48550\/arXiv.2107.08430","DOI":"10.48550\/arXiv.2107.08430"},{"key":"655_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101845","volume":"88","author":"DR Loh","year":"2021","unstructured":"Loh, D.R., Yong, W.X., Yapeter, J., Subburaj, K., Chandramohanadas, R.: A deep learning approach to the screening of malaria infection: automated and rapid cell counting, object detection and instance segmentation using mask R-CNN. Comput. Med. Imaging Graph. 88, 101845 (2021). https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101845","journal-title":"Comput. Med. Imaging Graph."},{"issue":"5","key":"655_CR22","doi-asserted-by":"publisher","first-page":"340","DOI":"10.3390\/machines10050340","volume":"10","author":"AI Xavier","year":"2022","unstructured":"Xavier, A.I., Villavicencio, C., Macrohon, J.J., Jeng, J., Hsieh, J.: Object detection via gradient-based mask R-CNN using machine learning algorithms. Machines 10(5), 340 (2022). https:\/\/doi.org\/10.3390\/machines10050340","journal-title":"Machines"},{"issue":"2","key":"655_CR23","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1109\/TBDATA.2022.3187413","volume":"9","author":"X Bi","year":"2022","unstructured":"Bi, X., Hu, J., Xiao, B., Li, W.S., Gao, X.B.: IEMask R-CNN: information-enhanced mask R-CNN. IEEE Trans. Big Data 9(2), 688\u2013700 (2022). https:\/\/doi.org\/10.1109\/TBDATA.2022.3187413","journal-title":"IEEE Trans. Big Data"},{"key":"655_CR24","doi-asserted-by":"publisher","first-page":"43603","DOI":"10.1109\/ACCESS.2023.3271895","volume":"11","author":"C Santos","year":"2023","unstructured":"Santos, C., Aguiar, M., Welfer, D., Dias, M., Pereira, A., Ribeiro, M., Belloni, B.: A new approach for fundus lesions instance segmentation based on mask R-CNN X101-FPN pre-trained architecture. IEEE Access 11, 43603\u201343618 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3271895","journal-title":"IEEE Access"}],"updated-by":[{"DOI":"10.1007\/s44196-024-00682-7","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000}}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00655-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00655-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00655-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T13:05:42Z","timestamp":1729688742000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00655-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["655"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00655-w","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s44196-024-00682-7","asserted-by":"object"}]},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"11 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s44196-024-00682-7","URL":"https:\/\/doi.org\/10.1007\/s44196-024-00682-7","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or nonfinancial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable, as the study did not require ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"255"}}