{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:16:09Z","timestamp":1769847369533,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T00:00:00Z","timestamp":1629936000000},"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":["41977221,41972267,41202197"],"award-info":[{"award-number":["41977221,41972267,41202197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011789","name":"Department of Science and Technology of Jilin Province","doi-asserted-by":"publisher","award":["20190303103SF,20170101001JC"],"award-info":[{"award-number":["20190303103SF,20170101001JC"]}],"id":[{"id":"10.13039\/501100011789","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods.<\/jats:p>","DOI":"10.3390\/rs13173383","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:59:45Z","timestamp":1630447185000},"page":"3383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8611-1101","authenticated-orcid":false,"given":"Shengwu","family":"Qin","sequence":"first","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Xu","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4493-3705","authenticated-orcid":false,"given":"Jingbo","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Shuangshuang","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Lingshuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-9541","authenticated-orcid":false,"given":"Jingyu","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Qiushi","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]},{"given":"Yanqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e3998","DOI":"10.1002\/ett.3998","article-title":"Review on remote sensing methods for landslide detection using machine and deep learning","volume":"32","author":"Mohan","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1029\/2000WR900090","article-title":"Landslide triggering by rain infiltration","volume":"36","author":"Iverson","year":"2000","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.scitotenv.2019.03.415","article-title":"The human cost of global warming: Deadly landslides and their triggers (1995\u20132014)","volume":"682","author":"Haque","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5775","DOI":"10.1007\/s10064-019-01504-3","article-title":"Predicting debris-flow clusters under extreme rainstorms: A case study on Hong Kong Island","volume":"78","author":"Zhou","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.3390\/rs4051310","article-title":"A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories","volume":"4","author":"Antolini","year":"2012","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s10346-020-01353-2","article-title":"Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks","volume":"17","author":"Ji","year":"2020","journal-title":"Landslides"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Miele, P., Di Napoli, M., Guerriero, L., Ramondini, M., Sellers, C., Corona, M.A., and Di Martire, D. (2021). Landslide Awareness System (LAwS) to Increase the Resilience and Safety of Transport Infrastructure: The Case Study of Pan-American Highway (Cuenca-Ecuador). Remote Sens., 13.","DOI":"10.3390\/rs13081564"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qi, T.J., Zhao, Y., Meng, X.M., Chen, G., and Dijkstra, T. (2021). AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China. Remote Sens., 13.","DOI":"10.3390\/rs13091819"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, B., He, K., Han, M., Hu, X.W., Ma, G.T., and Wu, M.Y. (2021). Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China. Remote Sens., 13.","DOI":"10.3390\/rs13091653"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s12524-018-0791-1","article-title":"Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier","volume":"46","author":"Pham","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (2016). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping, Assoc Advancement Artificial Intelligence.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qiao, S., Qin, S., Chen, J., Hu, X., and Ma, Z. (2019). The Application of a Three-Dimensional Deterministic Model in the Study of Debris Flow Prediction Based on the Rainfall-Unstable Soil Coupling Mechanism. Processes, 7.","DOI":"10.3390\/pr7020099"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11069-020-04498-4","article-title":"Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility","volume":"106","author":"Sun","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yao, J., Qin, S., Qiao, S., Che, W., Chen, Y., Su, G., and Miao, Q. (2020). Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China. Appl. Sci., 10.","DOI":"10.3390\/app10165640"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104426","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":"ref_16","doi-asserted-by":"crossref","unstructured":"Ding, A., Zhang, Q., Zhou, X., and Dai, B. (2016, January 11\u201313). Automatic recognition of landslide based on CNN and texture change detection. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.","DOI":"10.1109\/YAC.2016.7804935"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s12145-019-00413-z","article-title":"Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images","volume":"13","author":"Yang","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., and Loew, S. (2020). Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-11876"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114363","DOI":"10.1109\/ACCESS.2019.2935761","article-title":"Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information","volume":"7","author":"Sameen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/9109250","article-title":"Remote Sensing Landslide Recognition Based on Convolutional Neural Network","volume":"2019","author":"Wang","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Meena, S.R., Blaschke, T., and Aryal, J. (2019). UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11172046"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"165356","DOI":"10.1016\/j.ijleo.2020.165356","article-title":"Remote sensing image scene classification using CNN-MLP with data augmentation","volume":"221","author":"Shawky","year":"2020","journal-title":"Optik"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"23070","DOI":"10.1109\/ACCESS.2021.3055554","article-title":"Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data","volume":"9","author":"Censi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, H., Xie, C., Fei, Y., and Tao, H. (2021). Attention Mechanisms in CNN-Based Single Image Super-Resolution: A Brief Review and a New Perspective. Electronics, 10.","DOI":"10.3390\/electronics10101187"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, R., Tao, Y., Lu, Z., and Zhong, Y. (2018). Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101602"},{"key":"ref_28","first-page":"042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Chan","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lu, H., Ma, L., Fu, X., Liu, C., Wang, Z., Tang, M., 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"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8506","DOI":"10.1080\/01431161.2019.1615652","article-title":"Convolutional neural network based heterogeneous transfer learning for remote-sensing scene classification","volume":"40","author":"Zhao","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pires de Lima, R., and Marfurt, K. (2019). Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12010086"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tan, B., Zhang, Y., Pan, S.J., and Yang, Q. (2017). Distant Domain Transfer Learning, Assoc Advancement Artificial Intelligence.","DOI":"10.1609\/aaai.v31i1.10826"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, B., Song, Y., Zhong, E., and Yang, Q. (2015, January 10\u201313). Transitive Transfer Learning. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia.","DOI":"10.1145\/2783258.2783295"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Niu, S., Hu, Y., Wang, J., Liu, Y., and Song, H. (2020, January 10\u201313). Feature-based Distant Domain Transfer Learning. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378493"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision\u2014ECCV 2018, M\u00fcnchen, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","unstructured":"Hu, S., and Ye, X. (2013, January 20\u201322). GIS-based Rainfall-Triggered Landslide Warning and Forecasting Model of Shenzhen. Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-020-5071-z","article-title":"Geological environment problems during metro shield tunnelling in Shenzhen, China","volume":"13","author":"He","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10346-007-0112-1","article-title":"The rainfall intensity\u2013duration control of shallow landslides and debris flows: An update","volume":"5","author":"Guzzetti","year":"2008","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.scitotenv.2018.12.074","article-title":"Landslides-oriented urban disaster resilience assessment-A case study in ShenZhen, China","volume":"661","author":"Zhang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1007\/s10346-019-01239-y","article-title":"How does a cluster of buildings affect landslide mobility: A case study of the Shenzhen landslide","volume":"16","author":"Luo","year":"2019","journal-title":"Landslides"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2013.2250293","article-title":"Semiautomatic Object-Oriented Landslide Recognition Scheme From Multisensor Optical Imagery and DEM","volume":"52","author":"Rau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, B., Zhong, Y.F., Zhang, L.P., and Huang, B. (2016). The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification. Remote Sens., 8.","DOI":"10.3390\/rs8020157"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","article-title":"Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/LGRS.2010.2055033","article-title":"Satellite Image Classification via Two-Layer Sparse Coding with Biased Image Representation","volume":"8","author":"Dai","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","article-title":"Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images","volume":"22","author":"Wang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TCE.2007.4429280","article-title":"Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement","volume":"53","author":"Ibrahim","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/978-3-319-64698-5_4","article-title":"A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework","volume":"Volume 10425","author":"Felsberg","year":"2017","journal-title":"Computer Analysis of Images and Patterns: 17th International Conference, Caip 2017, Pt II"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ying, Z., Li, G., Ren, Y., Wang, R., and Wang, W. (2017, January 22\u201329). A New Low-Light Image Enhancement Algorithm Using Camera Response Model. Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.356"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A Decade Survey of Transfer Learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tan, B., Yu, Z., Pan, S.J., and Qiang, Y. (2017, January 4\u20139). Distant Domain Transfer Learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10826"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A Survey on Deep Transfer Learning. Artificial Neural Networks and Machine Learning\u2014ICANN 2018, Springer.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Notti, D., Giordan, D., Cina, A., Manzino, A., Maschio, P., and Bendea, I.H. (2021). Debris Flow and Rockslide Analysis with Advanced Photogrammetry Techniques Based on High-Resolution RPAS Data. Ponte Formazza Case Study (NW Alps). Remote Sens., 13.","DOI":"10.3390\/rs13091797"},{"key":"ref_58","first-page":"1-1","article-title":"Distant Domain Transfer Learning for Medical Imaging","volume":"21","author":"Niu","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.procs.2016.07.144","article-title":"Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning","volume":"91","author":"Liu","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Turchenko, V., Chalmers, E., and Luczak, A. (2017). A Deep Convolutional Auto-Encoder with Pooling\u2014Unpooling Layers in Caffe. arXiv.","DOI":"10.1109\/IDAACS.2017.8095172"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s10346-020-01513-4","article-title":"Landslide detection by deep learning of non-nadiral and crowdsourced optical images","volume":"18","author":"Catani","year":"2020","journal-title":"Landslides"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (15\u201316, January 8\u201310). Deep CORAL: Correlation Alignment for Deep Domain Adaptation. Proceedings of the Computer Vision\u2014ECCV 2016 Workshops, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"E49","DOI":"10.1093\/bioinformatics\/btl242","article-title":"Integrating structured biological data by Kernel Maximum Mean Discrepancy","volume":"22","author":"Borgwardt","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.catena.2012.06.012","article-title":"Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China","volume":"99","author":"Bai","year":"2012","journal-title":"Catena"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s11069-013-0661-7","article-title":"Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China","volume":"68","author":"Xu","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cheng, G., Ma, C., Zhou, P., Yao, X., and Han, J. (2016, January 10\u201315). Scene classification of high resolution remote sensing images using convolutional neural networks. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729193"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chen, Y., Qin, S., Qiao, S., Dou, Q., Che, W., Su, G., Yao, J., and Nnanwuba, U.E. (2020). Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China. Water, 12.","DOI":"10.3390\/w12082079"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:52:27Z","timestamp":1760165547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,26]]},"references-count":71,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173383"],"URL":"https:\/\/doi.org\/10.3390\/rs13173383","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,26]]}}}