{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T04:06:28Z","timestamp":1768709188372,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"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":["41930102"],"award-info":[{"award-number":["41930102"]}],"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":["41671389"],"award-info":[{"award-number":["41671389"]}],"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":["41601411"],"award-info":[{"award-number":["41601411"]}],"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":["41801321"],"award-info":[{"award-number":["41801321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23100102"],"award-info":[{"award-number":["XDA23100102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran\u2019s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.<\/jats:p>","DOI":"10.3390\/rs12050793","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping"],"prefix":"10.3390","volume":"12","author":[{"given":"Hu","family":"Ding","sequence":"first","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"},{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"}]},{"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Xiaozheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"},{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7930-3319","authenticated-orcid":false,"given":"Liyang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"},{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Guoan","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China"},{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3999-1174","authenticated-orcid":false,"given":"Fang","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Geospatial Science, University of Texas at Dallas, Richardson, TX 75080-3021, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6234-9812","authenticated-orcid":false,"given":"Josef","family":"Strobl","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2013Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0341-8162(02)00143-1","article-title":"Gully erosion and environmental change: Importance and research needs","volume":"50","author":"Poesen","year":"2003","journal-title":"Catena"},{"key":"ref_2","unstructured":"Upper and Middle Yellow River Bureau (2012). Atlas of Soil and Water Conservation in the Yellow River Basin, Seismological Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1002\/esp.4332","article-title":"Gully Morphological Characteristics in the Loess Hilly-gully Region Based on 3D Laser Scanning Technique","volume":"43","author":"Wu","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11442-016-1279-y","article-title":"Capacity of soil loss control in the Loess Plateau based on soil erosion control degree","volume":"26","author":"Gao","year":"2016","journal-title":"J. Geogr. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s11431-008-5002-9","article-title":"Research on the slope spectrum of the Loess Plateau","volume":"51","author":"Tang","year":"2008","journal-title":"Sci. China Ser. E"},{"key":"ref_6","first-page":"48","article-title":"A review of gully erosion process research","volume":"47","author":"Zheng","year":"2016","journal-title":"Trans. CSAE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.catena.2016.04.018","article-title":"A gully erosion assessment model for the Chinese Loess Plateau based on changes in gully length and area","volume":"148","author":"Li","year":"2017","journal-title":"Catena"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.geomorph.2018.04.011","article-title":"Large-scale mapping of gully-affected areas: An approach integrating google earth images and terrain skeleton information","volume":"314","author":"Liu","year":"2018","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Y.X., Jiao, J.Y., Wei, Y.H., Zhao, H.K., Yu, W.J., Cao, B.T., Xu, H.Y., Yan, F.C., Wu, D.Y., and Li, H. (2019). Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China. Int. J. Env. Res. Pub. Health, 16.","DOI":"10.3390\/ijerph16030369"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.geomorph.2014.10.005","article-title":"Assessment of bank gully development and vegetation coverage on the Chinese Loess Plateau","volume":"228","author":"Li","year":"2015","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.catena.2005.06.002","article-title":"Monitoring of gully erosion on the Loess Plateau of China using a global positioning system","volume":"63","author":"Wu","year":"2005","journal-title":"Catena"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.geomorph.2011.07.003","article-title":"Object-based gully feature extraction using high spatial resolution imagery","volume":"134","author":"Shruthi","year":"2011","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.catena.2013.09.004","article-title":"Soil erosion in gully catchments affected by land-levelling measures in the Souss Basin, Morocco, analysed by rainfall simulation and UAV remote sensing data","volume":"113","author":"Peter","year":"2014","journal-title":"Catena"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.geomorph.2014.04.006","article-title":"Object-based gully system prediction from medium resolution imagery using Random Forests","volume":"216","author":"Shruthi","year":"2014","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8287","DOI":"10.3390\/rs6098287","article-title":"Detection of gully-affected areas by applying Object-Based Image Analysis (OBIA) in the region of Taroudannt, Morocco","volume":"6","author":"Marzolff","year":"2014","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, K., Ding, H., Tang, G.A., Na, J.M., Huang, X.L., Xue, Z.G., Yang, X., and Li, F.Y. (2016). Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau. ISPRS J. Photogramm. Remote Sens., 5.","DOI":"10.3390\/ijgi5120238"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11769-017-0874-x","article-title":"An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery: A Case Study on Hilly Loess Plateau Region, China","volume":"27","author":"Liu","year":"2017","journal-title":"Chin. Geogr. Sci."},{"key":"ref_18","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution segmentation. Angewandte Geographische Informationsverarbeitung XII, Herbert Wichmann Verlag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1080\/15481603.2016.1215769","article-title":"A comparative study of the segmentation of weighted aggregation and multiresolution segmentation","volume":"53","author":"Du","year":"2016","journal-title":"Gisci. Remote. Sens."},{"key":"ref_21","unstructured":"Xiong, Z.Q., Zhang, X.Y., Wang, X.N., and Yuan, J. (2018). Self-adaptive segmentation of satellite images based on a weighted aggregation approach. Gisci. Remote. Sens., 1\u201323."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1109\/83.887975","article-title":"A hierarchical approach to color image segmentation using homogeneity","volume":"9","author":"Cheng","year":"2000","journal-title":"IEEE T. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Camilus, K.S., and Govindan, V.K. (2012). A Review on Graph Based Segmentation. I. J. Image Graph. Signal Process., 4.","DOI":"10.5815\/ijigsp.2012.05.01"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10851-010-0223-5","article-title":"Normalized cuts revisited: A reformulation for segmentation with linear grouping constraints","volume":"39","author":"Eriksson","year":"2011","journal-title":"J. Math Imaging Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Grady, L. (2006). Random walks for image segmentation. IEEE T. Pattern Anal., 1768\u20131783.","DOI":"10.1109\/TPAMI.2006.233"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cviu.2008.07.008","article-title":"Topology cuts: A novel min-cut\/max-flow algorithm for topology preserving segmentation in N\u2013D images","volume":"112","author":"Zeng","year":"2008","journal-title":"Comput. Vis. Image Und."},{"key":"ref_28","unstructured":"Sharon, E., Brandt, A., and Basri, R. (2000, January 13\u201315). Fast multiscale image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000), Hilton Head Island, SC, USA."},{"key":"ref_29","unstructured":"Sharon, E., Brandt, A., and Basri, R. (2001, January 8\u201314). Segmentation and boundary detection using multiscale intensity measurements. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1038\/nature04977","article-title":"Hierarchy and adaptivity in segmenting visual scenes","volume":"442","author":"Sharon","year":"2006","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X.Y., Du, S.H., and Ming, D.P. (2018). Segmentation Scale Selection in geographic object-based image analysis. High Spatial Resolution Remote Sensing: Data, Analysis, and Applications, CRC Press. [1st ed.].","DOI":"10.1201\/9780429470196-10"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1016\/j.rse.2016.03.015","article-title":"Learning selfhood scales for urban land cover mapping with very-high-resolution satellite images","volume":"178","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3694","DOI":"10.1109\/TGRS.2014.2381632","article-title":"Toward evaluating multiscale segmentations of high spatial resolution remote sensing images","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, L., Ming, D.P., Zhou, W., Bao, H.Q., Chen, Y.Y., and Ling, X. (2019). Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11020108"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"629","DOI":"10.14358\/PERS.84.10.629","article-title":"Review on high spatial resolution remote sensing image segmentation evaluation","volume":"84","author":"Chen","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","first-page":"523","article-title":"Drainage basin object-based method for regional-scale landform classification: A case study of loess area in China","volume":"39","author":"Xiong","year":"2018","journal-title":"Phys. Geogr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jozdani, S.E., Johnson, B.A., and Chen, D. (2019). Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use\/Land Cover Classification. Remote Sens., 11.","DOI":"10.3390\/rs11141713"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: Reliable Large-Scale Tree Boosting System. arXiv, 1\u20136.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ecolind.2012.03.021","article-title":"The Grain for Green Project induced land cover change in the Loess Plateau: A case study with Ansai County, Shanxi Province, China","volume":"23","author":"Zhou","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1002\/hyp.3360050107","article-title":"On the extraction of channel networks from digital elevation data","volume":"5","author":"Tarboton","year":"1991","journal-title":"Hydro. Process."},{"key":"ref_42","first-page":"1593","article-title":"Extracting topographic structure from digital elevation data for geographic information system analysis","volume":"54","author":"Jenson","year":"1988","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1029\/TR038i006p00913","article-title":"Quantitative analysis of watershed geomorphology","volume":"38","year":"1957","journal-title":"Transactions American Geophsical Union"},{"key":"ref_44","first-page":"913","article-title":"An adaptive approach to selecting accumulation threshold for gully networks extraction from DEMs","volume":"33","author":"Wu","year":"2017","journal-title":"Geo. GeoInfo. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S., Johnson, B., and Wolff, E. (2018). Scale matters: Spatially partitioned unsupervised segmentation parameter optimization for large and heterogeneous satellite images. Remote Sens., 10.","DOI":"10.3390\/rs10091440"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.3390\/ijgi4042292","article-title":"Image segmentation parameter optimization considering within- and between-segment heterogeneity at multiple scale levels: Test case for mapping residential areas using landsat imagery","volume":"4","author":"Johnson","year":"2015","journal-title":"ISPRS Int. GeoInf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Y., Meng, Q., Qi, Q., Yang, J., and Liu, Y. (2018). Region merging considering within- and between-segment heterogeneity: An improved hybrid remote-sensing image segmentation method. Remote Sens., 10.","DOI":"10.3390\/rs10050781"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2015.01.009","article-title":"Segmentation quality evaluation using region-based precision and recall measures for remote sensing images","volume":"102","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Accuracy assessment measures for object-based image segmentation goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2017","journal-title":"Gisci. Remote. Sens."},{"key":"ref_52","unstructured":"Chen, T., and Tong, H. (2015). Xgboost: Extreme Gradient Boosting. R Package Version 0.4-2, CRAN R Package."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1703-0","article-title":"Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: Advantages and limitations","volume":"79","author":"Conoscenti","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approach to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_56","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-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2018.03.017","article-title":"Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield","volume":"317","author":"Bugnicourt","year":"2018","journal-title":"Geomorphology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s11629-017-4551-4","article-title":"Stability analysis unit and spatial distribution pattern of the terrain texture in the northern Shaanxi Loess Plateau","volume":"15","author":"Ding","year":"2018","journal-title":"J. Mt. Sci."},{"key":"ref_59","first-page":"1","article-title":"Bioactive Molecule Prediction Using Extreme Gradient Boosting","volume":"21","author":"Mustapha","year":"2016","journal-title":"Molecules"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11707-018-0700-5","article-title":"Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China","volume":"13","author":"Yang","year":"2019","journal-title":"Front Earth Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, S.J., Xiong, L.Y., Tang, G.A., and Strobl, J. (2020). Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery. Geomorphology, 107045.","DOI":"10.1016\/j.geomorph.2020.107045"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhang, W., Witharana, C., Liljedahl, A.K., and Kanevskiy, M. (2018). Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091487"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.geomorph.2014.03.008","article-title":"High-resolution topography for understanding Earth surface processes: Opportunities and challenges","volume":"216","author":"Tarolli","year":"2014","journal-title":"Geomorphology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1002\/esp.3673","article-title":"Multi-temporal UAV data for automatic measurement of rill and interrill erosion on loess soil","volume":"40","author":"Eltner","year":"2015","journal-title":"Earth Surf. Process. Lanf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:09Z","timestamp":1760173389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,2]]},"references-count":64,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050793"],"URL":"https:\/\/doi.org\/10.3390\/rs12050793","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,2]]}}}