{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:20:35Z","timestamp":1780928435747,"version":"3.54.1"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China","award":["ZDJ2021\u221214"],"award-info":[{"award-number":["ZDJ2021\u221214"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China","award":["NORSLS20\u221207"],"award-info":[{"award-number":["NORSLS20\u221207"]}]},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China","award":["2018YFC1504703"],"award-info":[{"award-number":["2018YFC1504703"]}]},{"name":"Lhasa National Geophysical Observation and Research Station","award":["ZDJ2021\u221214"],"award-info":[{"award-number":["ZDJ2021\u221214"]}]},{"name":"Lhasa National Geophysical Observation and Research Station","award":["NORSLS20\u221207"],"award-info":[{"award-number":["NORSLS20\u221207"]}]},{"name":"Lhasa National Geophysical Observation and Research Station","award":["2018YFC1504703"],"award-info":[{"award-number":["2018YFC1504703"]}]},{"name":"National Key Research and Development Program of China","award":["ZDJ2021\u221214"],"award-info":[{"award-number":["ZDJ2021\u221214"]}]},{"name":"National Key Research and Development Program of China","award":["NORSLS20\u221207"],"award-info":[{"award-number":["NORSLS20\u221207"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFC1504703"],"award-info":[{"award-number":["2018YFC1504703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efficient detection of earthquake\u2212triggered landslides is crucial for emergency response and risk assessment. With the development of multi\u2212source remote sensing images, artificial intelligence has gradually become a powerful landslide detection method for similar tasks, aiming to mitigate time\u2212consuming problems and meet emergency requirements. In this study, a relatively new deep learning (DL) network, called U\u2212Net++, was designed to detect landslides for regions affected by the Iburi, Japan Mw = 6.6 earthquake, with only small training samples. For feature extraction, ResNet50 was selected as the feature extraction layer, and transfer learning was adopted to introduce the pre\u2212trained weights for accelerating the model convergence. To prove the feasibility and validity of the proposed model, the random forest algorithm (RF) was selected as the benchmark, and the F1\u2212score, Kappa coefficient, and IoU (Intersection of Union) were chosen to quantitatively evaluate the model\u2019s performance. In addition, the proposed model was trained with different sample sizes (256,512) and network depths (3,4,5), respectively, to analyze their impacts on performance. The results showed that both models detected the majority of landslides, while the proposed model obtained the highest metric value (F1\u2212score = 0.7580, Kappa = 0.7441, and IoU = 0.6104) and was capable of resisting the noise. In addition, the proposed model trained with sample size 256 possessed optimal performance, proving that the size is a non\u2212negligible parameter in U\u2212Net++, and it was found that the U\u2212Net++ trained with shallower layer 3 yielded better results than that with the standard layer 5. Finally, the outstanding performance of the proposed model on a public landslide dataset demonstrated the generalization of U\u2212Net++.<\/jats:p>","DOI":"10.3390\/rs14122826","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T06:31:59Z","timestamp":1655101919000},"page":"2826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Efficient Detection of Earthquake\u2212Triggered Landslides Based on U\u2212Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 Earthquake"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3980-3436","authenticated-orcid":false,"given":"Zhiqiang","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management, Beijing 100085, China"},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3956-4925","authenticated-orcid":false,"given":"Chong","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management, Beijing 100085, China"},{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1007\/s11430-013-4582-9","article-title":"Dynamic mechanisms of earthquake\u2212triggered landslides","volume":"56","author":"Zhu","year":"2013","journal-title":"China Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1130\/0016-7606(1984)95<406:LCBE>2.0.CO;2","article-title":"Keffer. Landslides caused by earthquakes","volume":"95","author":"David","year":"1984","journal-title":"GSA Bulletin."},{"key":"ref_3","first-page":"1156","article-title":"Contribution of strata lithology and slope gradient to landslides triggered by Wenchuan Ms 8 earthquake, Sichuan, China","volume":"28","author":"Yao","year":"2009","journal-title":"Geol. Bull. China"},{"key":"ref_4","unstructured":"Xu, C. (2014, January 20\u201323). Catalogue of landslides and the amount of slope material lost due to the 2013 Lushan earthquake in China. Proceedings of the Annual Meeting of Chinese Geoscience Union (2014), Beijing, China."},{"key":"ref_5","first-page":"1069","article-title":"An updated database and spatial distribution of landslides triggered by the Milin, Tibet Mw6.4 Earthquake of 18 November 2017","volume":"32","author":"Huang","year":"2021","journal-title":"Earth Sci."},{"key":"ref_6","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. Geology."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1007\/s11629-017-4697-0","article-title":"Landslide inventory and susceptibility modelling using geospatial tools, in Hunza\u2212Nagar valley, northern Pakistan","volume":"15","author":"Bacha","year":"2018","journal-title":"Mt. Sci."},{"key":"ref_8","first-page":"509","article-title":"Earthquake\u2212induced landslide recognition using high-resolution remote sensing images","volume":"21","author":"Peng","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.enggeo.2005.10.006","article-title":"Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan","volume":"82","author":"Chigira","year":"2006","journal-title":"Eng. Geology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1080\/01431160512331314047","article-title":"Satellite remote sensing for detailed landslide inventories using change detection and image fusion","volume":"26","author":"Nichol","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hang, H., Tung, H., Hoa, P., Phuong, N., Phong, T., Costache, R., Nguyen, H., Amiri, M., Le, H., and Le, H. (2021). Spatial prediction of landslides along National Highway\u22126, Hoa Binh province, Vietnam using novel hybrid models. Geocarto Int., 1\u201326.","DOI":"10.1080\/10106049.2021.1912195"},{"key":"ref_12","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_13","first-page":"225","article-title":"An overview on earthquake\u2212induced landslide research","volume":"19","author":"Zhang","year":"2013","journal-title":"J. Geomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.geomorph.2010.07.026","article-title":"Spatiotemporal landslide detection for the 2005 Kashmir earthquake region","volume":"124","author":"Saba","year":"2010","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.geomorph.2010.12.030","article-title":"Distribution pattern of earthquake\u2212induced landslides triggered by the 12 May 2008 Wenchuan earthquake","volume":"133","author":"Gorum","year":"2011","journal-title":"Geomorphology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10346-006-0069-5","article-title":"Interpretation of landslide distribution triggered by the 2005 Northern Pakistan earthquake using SPOT\u22125 imagery","volume":"4","author":"Sato","year":"2007","journal-title":"Landslides"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, S., and Hua, H. (2009, January 17\u201319). Automatic recognition of landslides based on change detection. Proceedings of the International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Imaging Detectors and Applications, Beijing, China.","DOI":"10.1117\/12.836109"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","article-title":"Semi\u2212automated 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_20","doi-asserted-by":"crossref","unstructured":"Plank, S., Twele, A., and Martinis, S. (2016). Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040307"},{"key":"ref_21","first-page":"2918","article-title":"Classification of landslide surfaces using fully polarimetric SAR: Examples from Taiwan","volume":"5","author":"Rodriguez","year":"2002","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.3390\/rs4082314","article-title":"Polarimetric Decomposition Analysis of ALOS PALSAR Observation Data before and after a Landslide Event","volume":"4","author":"Yonezawa","year":"2012","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15424","DOI":"10.3390\/rs71115424","article-title":"Polarimetric Scattering Properties of Landslides in Forested Areas and the Dependence on the Local Incidence Angle","volume":"7","author":"Shibayama","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14576","DOI":"10.3390\/rs71114576","article-title":"Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy)","volume":"7","author":"Raspini","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","first-page":"275","article-title":"An introduction to decision tree modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom. A J. Chemom. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int J Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_31","first-page":"381","article-title":"Machine Learning Algorithms\u2014A Review","volume":"9","author":"Mahesh","year":"2019","journal-title":"Int. J. Sci. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101211","DOI":"10.1016\/j.gsf.2021.101211","article-title":"Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization","volume":"12","author":"Zhou","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, T., Trinder, J.C., and Niu, R. (2017). Object\u2212Oriented Landslide Mapping Using ZY\u22123 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three\u2212Gorges Reservoir, China. Remote Sens., 9.","DOI":"10.3390\/rs9040333"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep\u2212Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cviu.2017.04.002","article-title":"Hough\u2212CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound","volume":"164","author":"Milletari","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"Digit. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10064-020-01922-8","article-title":"Landslide susceptibility mapping using hybridized block modular intelligence model","volume":"80","author":"Shahri","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_39","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_40","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_41","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_42","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U\u2212Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the 18th Medical Image Computing and Computer\u2212Assisted Intervention (MICCAI 2015), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_43","unstructured":"Soares, L., Dias, H., and Grohmann, C. (2020). Landslide Segmentation with U\u2212Net: Evaluating Different Sampling Methods and Patch Sizes. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, P., Xu, C., Ma, S., Shao, X., Tian, Y., and Wen, B. (2020). Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12233992"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qi, W., Wei, M., Yang, W., Xu, C., and Ma, C. (2020). Automatic Mapping of Landslides by the ResU\u2212Net. Remote Sens., 12.","DOI":"10.3390\/rs12152487"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A New Deep\u2212Learning\u2212Based Approach for Earthquake\u2212Triggered Landslide Detection from Single\u2212Temporal RapidEye Satellite Imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s10346-020-01557-6","article-title":"Deep convolutional neural network\u2013based pixel\u2212wise landslide inventory mapping","volume":"18","author":"Su","year":"2021","journal-title":"Landslides"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14629","DOI":"10.1038\/s41598-021-94190-9","article-title":"A comprehensive transferability evaluation of U\u2212Net and ResU\u2212Net for landslide detection from Sentinel\u22122 data (case study areas from Taiwan, China, and Japan)","volume":"11","author":"Ghorbanzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Gholamnia, K., and Ghamisi, P. (2022). The application of ResU\u2212net and OBIA for landslide detection from multi\u2212temporal sentinel\u22122 images. Big Earth Data, 1\u201326.","DOI":"10.1080\/20964471.2022.2031544"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object\u2212based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Rahimzad, M., Homayouni, S., Alizadeh Naeini, A., and Nadi, S. (2021). An Efficient Multi\u2212Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE). Remote Sens., 13.","DOI":"10.3390\/rs13132501"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Rahimzad, M., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Homayouni, S., Blaschke, T., Lim, S., and Ghamisi, P. (2021). Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel\u22122 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224698"},{"key":"ref_54","unstructured":"Zhou, Z., Rahman Siddiquee, M., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A Nested U\u2212Net Architecture for Medical Image Segmentation. Proceedings of the 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML\u2212CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2113\/gseegeosci.8.1.35","article-title":"Characteristics of deep\u2212seated landslides of Hokkaido: Analyses of a database of landslides of Hokkaido, Japan","volume":"8","author":"Yamagishi","year":"2002","journal-title":"Environ. Eng. Geosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1007\/s10346-018-1092-z","article-title":"Landslides by the 2018 Hokkaido Iburi\u2212Tobu Earthquake on September 6","volume":"15","author":"Yamagishi","year":"2018","journal-title":"Landslides"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1007\/s10346-019-01207-6","article-title":"Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake, Northern Japan","volume":"16","author":"Zhang","year":"2019","journal-title":"Landslides"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shao, X., Ma, S., Xu, C., Zhang, P., Wen, B., Tian, Y., Zhou, Q., and Cui, Y. (2019). Planet Image\u2212Based Inventorying and Machine Learning\u2212Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake. Remote Sens., 11.","DOI":"10.3390\/rs11080978"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random Forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int J Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jaiswal, J., and Samikannu, R. (2017, January 2\u20134). Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression. Proceedings of the World Congress on Computing and Communication Technologies (WCCCT,2017), Tiruchirappalli, India.","DOI":"10.1109\/WCCCT.2016.25"},{"key":"ref_61","unstructured":"Dalal, N., and Triggs, B. (2015, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_63","unstructured":"Santurkar, S., Tsipras, D., Ilyas, A., and Madry, A. (2018). How does batch normalization help optimization?. arXiv."},{"key":"ref_64","unstructured":"Lu, L., Shin, Y., Su, Y., and Karniadakis, G.E. (2019). Dying relu and initialization: Theory and numerical examples. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S. (2016, January 25\u201328). V\u2212Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Garcia\u2212Garcia, A., Orts\u2212Escolano, S., Oprea, S., Villena\u2212Martinez, V., and Garcia\u2212Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/978-3-540-39985-8_12","article-title":"Confusion matrix visualization","volume":"25","author":"Susmaga","year":"2004","journal-title":"Intelligent Information Processing and Web Mining"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"104954","DOI":"10.1016\/j.envsoft.2020.104954","article-title":"The future of sensitivity analysis: An essential discipline for systems modeling and policy support","volume":"137","author":"Razavi","year":"2021","journal-title":"Environ. Model. Softw."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.envsoft.2019.01.012","article-title":"Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices","volume":"114","author":"Saltelli","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"562","DOI":"10.2166\/hydro.2020.098","article-title":"Updating the neural network sediment load models using different sensitivity analysis methods: A regional application","volume":"22","author":"Asheghi","year":"2020","journal-title":"J. Hydroinform."},{"key":"ref_71","unstructured":"Yakubovskiy, P., and Segmentation Models Pytorch (2022, March 11). GitHub Repository. Available online: https:\/\/github.com\/qubvel\/segmentation_models.pytorch."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:28:38Z","timestamp":1760138918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122826"],"URL":"https:\/\/doi.org\/10.3390\/rs14122826","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,13]]}}}