{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:02:47Z","timestamp":1784037767901,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41929001"],"award-info":[{"award-number":["41929001"]}]},{"name":"National Natural Science Foundation of China","award":["41874005"],"award-info":[{"award-number":["41874005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM.<\/jats:p>","DOI":"10.3390\/rs14215517","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuerong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5730-9602","authenticated-orcid":false,"given":"Chaoying","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resource and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2258-0993","authenticated-orcid":false,"given":"Jiangbo","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resource and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Lu","sequence":"additional","affiliation":[{"name":"Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3088-1481","authenticated-orcid":false,"given":"Shunping","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liquan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.enggeo.2014.08.015","article-title":"Heavy Rainfall Triggered Loess\u2013Mudstone Landslide and Subsequent Debris Flow in Tianshui, China","volume":"186","author":"Peng","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_2","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":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide Inventory Maps: New Tools for an Old Problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.enggeo.2010.06.013","article-title":"Landslide Inventories: The Essential Part of Seismic Landslide Hazard Analyses","volume":"122","author":"Harp","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s10346-020-01405-7","article-title":"The Future of Landslides\u2019 Past\u2014A Framework for Assessing Consecutive Landsliding Systems","volume":"17","author":"Temme","year":"2020","journal-title":"Landslides"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/isprs-archives-XLVI-4-W2-2021-205-2021","article-title":"An Overview of Geoinformatics State-of-the-Art Techniques for Landslide Monitoring and Mapping","volume":"XLVI-4\/W2-2021","author":"Yordanov","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"405","DOI":"10.5194\/nhess-18-405-2018","article-title":"Criteria for the Optimal Selection of Remote Sensing Optical Images to Map Event Landslides","volume":"18","author":"Fiorucci","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","article-title":"Landslide Mapping with Remote Sensing: Challenges and Opportunities","volume":"41","author":"Zhong","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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, China","volume":"30","author":"Tian","year":"2019","journal-title":"J. Earth Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S0169-555X(03)00056-4","article-title":"Monitoring Landslides from Optical Remotely Sensed Imagery: The Case History of Tessina Landslide, Italy","volume":"54","author":"Barredo","year":"2003","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1016\/j.rse.2011.03.006","article-title":"Semi-Automatic Recognition and Mapping of Rainfall Induced Shallow Landslides Using Optical Satellite Images","volume":"115","author":"Mondini","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","article-title":"Semi-Automated Landslide Inventory Mapping from Bitemporal Aerial Photographs Using Change Detection and Level Set Method","volume":"175","author":"Li","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1080\/19475705.2014.898702","article-title":"Rule-Based Semi-Automated Approach for the Detection of Landslides Induced by 18 September 2011 Sikkim, Himalaya, Earthquake Using IRS LISS3 Satellite Images","volume":"7","author":"Siyahghalati","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ramos-Bernal, R., V\u00e1zquez-Jim\u00e9nez, R., Romero-Calcerrada, R., Arrogante-Funes, P., and Novillo, C. (2018). Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10121987"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s10346-019-01178-8","article-title":"Using Sentinel-2 Time Series to Detect Slope Movement before the Jinsha River Landslide","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1038\/ngeo1154","article-title":"Mass Wasting Triggered by the 2008 Wenchuan Earthquake Is Greater than Orogenic Growth","volume":"4","author":"Parker","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.3390\/rs70404318","article-title":"Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm","volume":"7","author":"Dou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8026","DOI":"10.3390\/rs6098026","article-title":"Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data","volume":"6","author":"Behling","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1109\/TGRS.2013.2262052","article-title":"Active Learning in the Spatial Domain for Remote Sensing Image Classification","volume":"52","author":"Stumpf","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Alizadeh, M., Chen, W., Mohammadi, A., Ahmad, B., Panahi, M., and Hong, H. (2018). Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia. Remote Sens., 10.","DOI":"10.3390\/rs10101527"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.rse.2016.11.007","article-title":"Correlation of Satellite Image Time-Series for the Detection and Monitoring of Slow-Moving Landslides","volume":"189","author":"Stumpf","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep Learning in Environmental Remote Sensing: Achievements and Challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","article-title":"Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities","volume":"223","author":"Ma","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, Y., Ouyang, C., Zhang, F., and Ma, J. (2018). Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery. Sensors, 18.","DOI":"10.3390\/s18030821"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7700","DOI":"10.1080\/01431161.2020.1792577","article-title":"Interpretation and Use of Geomorphometry in Remote Sensing: A Guide and Review of Integrated Applications","volume":"41","author":"Franklin","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/LGRS.2018.2889307","article-title":"Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks","volume":"16","author":"Lei","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","first-page":"1747","article-title":"Automatic Objection of Loess Landslide Based on Deep Learning","volume":"45","author":"Ju","year":"2020","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_31","unstructured":"Jiang, W., Xi, J., Li, Z., Ding, M., Yang, L., and Xie, D. (2022). Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples. Geomat. Inf. Sci. Wuhan Univ., 1\u201318."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Qi, W., Wei, M., Yang, W., Xu, C., and Ma, C. (2020). Automatic Mapping of Landslides by the ResU-Net. Remote Sens., 12.","DOI":"10.3390\/rs12152487"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, P., Wei, Y., Wang, Q., Chen, Y., and Xie, J. (2020). Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model. Remote Sens., 12.","DOI":"10.3390\/rs12050894"},{"key":"ref_36","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."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4654","DOI":"10.1109\/TGRS.2020.3015826","article-title":"Landslide Recognition by Deep Convolutional Neural Network and Change Detection","volume":"59","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","first-page":"1","article-title":"Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TPAMI.2019.2929166","article-title":"Multiset Feature Learning for Highly Imbalanced Data Classification","volume":"43","author":"Jing","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10346-020-01602-4","article-title":"Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach","volume":"18","author":"Meena","year":"2021","journal-title":"Landslides"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep Learning for Cell Counting, Detection, and Morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yu, B., Chen, F., Xu, C., Wang, L., and Wang, N. (2021). Matrix SegNet: A Practical Deep Learning Framework for Landslide Mapping from Images of Different Areas with Different Spatial Resolutions. Remote Sens., 13.","DOI":"10.3390\/rs13163158"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:37Z","timestamp":1760144977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":46,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215517"],"URL":"https:\/\/doi.org\/10.3390\/rs14215517","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,2]]}}}