{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:27:01Z","timestamp":1773714421009,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"National Key R &amp; D Program of China","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"National Key R &amp; D Program of China","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"National Key R &amp; D Program of China","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"National Key R &amp; D Program of China","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"National Key R &amp; D Program of China","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]},{"name":"National Natural Science Foundation of China","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"National Natural Science Foundation of China","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"National Natural Science Foundation of China","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"National Natural Science Foundation of China","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"National Natural Science Foundation of China","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"international partnership program of the Chinese Academy of Sciences","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]},{"name":"Sichuan Science and Technology Program","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"Sichuan Science and Technology Program","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"Sichuan Science and Technology Program","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"Sichuan Science and Technology Program","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"Sichuan Science and Technology Program","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"Sichuan Science and Technology Program","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["42471086"],"award-info":[{"award-number":["42471086"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["52479058"],"award-info":[{"award-number":["52479058"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSERC Discovery","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"NSERC Discovery","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"NSERC Discovery","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"NSERC Discovery","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"NSERC Discovery","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"NSERC Discovery","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]},{"name":"Canada Foundation for Innovation","award":["2023YFC3006700"],"award-info":[{"award-number":["2023YFC3006700"]}]},{"name":"Canada Foundation for Innovation","award":["42471086"],"award-info":[{"award-number":["42471086"]}]},{"name":"Canada Foundation for Innovation","award":["52479058"],"award-info":[{"award-number":["52479058"]}]},{"name":"Canada Foundation for Innovation","award":["177GJHZ2022064FN"],"award-info":[{"award-number":["177GJHZ2022064FN"]}]},{"name":"Canada Foundation for Innovation","award":["2021YFH0028"],"award-info":[{"award-number":["2021YFH0028"]}]},{"name":"Canada Foundation for Innovation","award":["202006240220"],"award-info":[{"award-number":["202006240220"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management.<\/jats:p>","DOI":"10.3390\/rs16234614","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T11:16:48Z","timestamp":1733743008000},"page":"4614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0433-4177","authenticated-orcid":false,"given":"Wenqi","family":"Li","sequence":"first","affiliation":[{"name":"Changjiang River Scientific Research Institute, Wuhan 430010, China"},{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8918-1503","authenticated-orcid":false,"given":"Chendi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China"},{"name":"Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8684-5332","authenticated-orcid":false,"given":"David","family":"Puhl","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of British Columbia, Vancouver, BC V6T 1Z2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7845-0122","authenticated-orcid":false,"given":"Xiao","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada"},{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6856-5989","authenticated-orcid":false,"given":"Marwan A.","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of British Columbia, Vancouver, BC V6T 1Z2, Canada"}]},{"given":"Stephen","family":"Bird","sequence":"additional","affiliation":[{"name":"Fluvial Systems Research Inc., White Rock, BC V4B 0A7, Canada"}]},{"given":"Kejun","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Sichuan Zipingpu Development Co., Ltd., Chengdu 610091, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wohl, E. (2010). Mountain Rivers Revisited, American Geophysical Union.","DOI":"10.1029\/WM019"},{"key":"ref_2","unstructured":"Knighton, D. (1998). Fluvial Forms and Processes: A New Perspective, Routledge."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1002\/2014GL062764","article-title":"Patterns of river width and surface area revealed by the satellite-derived North American River Width data set","volume":"42","author":"Allen","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1126\/science.aat0636","article-title":"Global extent of rivers and streams","volume":"361","author":"Allen","year":"2018","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3252","DOI":"10.1029\/2019GL082027","article-title":"Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge","volume":"46","author":"Frasson","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112725","DOI":"10.1016\/j.rse.2021.112725","article-title":"Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface model simulations","volume":"267","author":"Gao","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1002\/esp.4788","article-title":"Co-evolution of coarse grain structuring and bed roughness in response to episodic sediment supply in an experimental aggrading channel","volume":"45","author":"Hassan","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1002\/esp.4815","article-title":"Width variations control the development of grain structuring in steep step-pool dominated streams: Insight from flume experiments","volume":"45","author":"Saletti","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1038\/s41467-024-46292-x","article-title":"Vegetation enhances curvature-driven dynamics in meandering rivers","volume":"15","author":"Finotello","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1029\/WR026i010p02353","article-title":"Width and Depth of Self-Formed Straight Gravel Rivers with Bank Vegetation","volume":"26","author":"Ikeda","year":"1990","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1002\/jgrf.20113","article-title":"Lithologic and tectonic controls on bedrock channel form at the northwest Himalayan front","volume":"118","author":"Allen","year":"2013","journal-title":"J. Geophys. Res.-Earth Surf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.geomorph.2009.07.017","article-title":"Assessment of morphological changes induced by flow and flood pulses in a gravel bed braided river: The Tagliamento River (Italy)","volume":"114","author":"Bertoldi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2020WR027949","DOI":"10.1029\/2020WR027949","article-title":"Constraining Remote River Discharge Estimation Using Reach-Scale Geomorphology","volume":"56","author":"Brinkerhoff","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"eabc1505","DOI":"10.1126\/sciadv.abc1505","article-title":"What sets river width?","volume":"6","author":"Dunne","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.envsoft.2018.03.028","article-title":"Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data","volume":"105","author":"Monegaglia","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1002\/esp.4582","article-title":"Testing models of step formation against observations of channel steps in a steep mountain stream","volume":"44","author":"Golly","year":"2019","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2022WR032796","DOI":"10.1029\/2022WR032796","article-title":"Experiments on the Sediment Transport Along Pool-Riffle Unit","volume":"58","author":"Hassan","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2020WR029090","DOI":"10.1029\/2020WR029090","article-title":"Spatial Distributions of At-Many-Stations Hydraulic Geometry for Mountain Rivers Originated From the Qinghai-Tibet Plateau","volume":"57","author":"Qin","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1038\/s43017-022-00282-z","article-title":"Threshold constraints on the size, shape and stability of alluvial rivers","volume":"3","author":"Phillips","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104526","DOI":"10.1016\/j.advwatres.2023.104526","article-title":"Theoretical derivation of hydraulic geometry equations for a gravel bed river channel","volume":"180","author":"Griffiths","year":"2023","journal-title":"Adv. Water Resour."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/esp.4478","article-title":"Active channel width as a proxy of sediment supply from mining sites in New Caledonia","volume":"44","author":"Bertrand","year":"2019","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/BF00204726","article-title":"River processes after rapid valley-filling due to large landslides","volume":"38","author":"Shimazu","year":"1996","journal-title":"Geojournal"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1130\/B25982.1","article-title":"Effects of sediment pulses on channel morphology in a gravel-bed river","volume":"119","author":"Hoffman","year":"2007","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.geomorph.2017.09.005","article-title":"Air-photo based change in channel width in the Minnesota River basin: Modes of adjustment and implications for sediment budget","volume":"297","author":"Lauer","year":"2017","journal-title":"Geomorphology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1002\/esp.3515","article-title":"The biogeomorphological life cycle of poplars during the fluvial biogeomorphological succession: A special focus on Populus nigra L.","volume":"39","author":"Corenblit","year":"2014","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.geomorph.2015.04.022","article-title":"Swiftness of biomorphodynamics in Lilliput- to Giant-sized rivers and deltas","volume":"244","author":"Kleinhans","year":"2015","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1002\/rra.2928","article-title":"A Conceptual Model of Vegetation\u2013hydrogeomorphology Interactions Within River Corridors","volume":"32","author":"Gurnell","year":"2016","journal-title":"River Res. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/24705357.2021.1892547","article-title":"Spatio-temporal distribution of Gymnocypris przewalskii during migration with UAV-based photogrammetry and deep neural network","volume":"7","author":"Zhang","year":"2022","journal-title":"J. Ecohydraulics"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Z.-Y., Lee, J.H.W., and Melching, C.S. (2015). River Dynamics and Integrated River Management, Springer Science & Business Media.","DOI":"10.1007\/978-3-642-25652-3"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1029\/WR014i006p01141","article-title":"Bank-full discharge of rivers","volume":"14","author":"Williams","year":"1978","journal-title":"Water Resour. Res."},{"key":"ref_31","first-page":"157","article-title":"A Study of the Bank-Full Discharges of Rivers in England and Wales","volume":"12","author":"Nixon","year":"1959","journal-title":"Proc. Inst. Civ. Eng.-Civ. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e21496","DOI":"10.1002\/wat2.1496","article-title":"Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change","volume":"8","author":"Boothroyd","year":"2020","journal-title":"WIREs Water"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2023WR035269","DOI":"10.1029\/2023WR035269","article-title":"Artificial Intelligence and Objective-Function Methods Can Identify Bankfull River Channel Extents","volume":"60","author":"Garber","year":"2024","journal-title":"Water Resour. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.03.011","article-title":"Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland","volume":"128","author":"Lu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Curcio, A.C., Peralta, G., Aranda, M., and Barbero, L. (2022). Evaluating the Performance of High Spatial Resolution UAV-Photogrammetry and UAV-LiDAR for Salt Marshes: The C\u00e1diz Bay Study Case. Remote Sens., 14.","DOI":"10.3390\/rs14153582"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xue, Y., Qin, C., Wu, B.S., Li, D., and Fu, X.D. (2022). Automatic Extraction of Mountain River Surface and Width Based on Multisource High-Resolution Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14102370"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4726","DOI":"10.1109\/JSTARS.2014.2309707","article-title":"River Delineation from Remotely Sensed Imagery Using a Multi-Scale Classification Approach","volume":"7","author":"Yang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1109\/LGRS.2015.2458898","article-title":"Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis","volume":"12","author":"Isikdogan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rse.2017.03.044","article-title":"RivaMap: An automated river analysis and mapping engine","volume":"202","author":"Isikdogan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"124689","DOI":"10.1016\/j.jhydrol.2020.124689","article-title":"Small Arctic rivers mapped from Sentinel-2 satellite imagery and ArcticDEM","volume":"584","author":"Lu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, G., Qin, C., and Wu, B.S. (2021). River Extraction under Bankfull Discharge Conditions Based on Sentinel-2 Imagery and DEM Data. Remote Sens., 13.","DOI":"10.3390\/rs13142650"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wei, Z.H., Jia, K.B., Liu, P.Y., Jia, X.W., Xie, Y.Q., and Jiang, Z. (2021). Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152893"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5614213","DOI":"10.1109\/TGRS.2021.3129789","article-title":"A Cascaded Spectral-Spatial CNN Model for Super-Resolution River Mapping With MODIS Imagery","volume":"60","author":"Yin","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5059","DOI":"10.1002\/wrcr.20398","article-title":"Hydraulic characterization of the middle reach of the Congo River","volume":"49","author":"Trigg","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"W10504","DOI":"10.1029\/2012WR011888","article-title":"Floodplain channel morphology and networks of the middle Amazon River","volume":"48","author":"Trigg","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/LGRS.2007.908305","article-title":"RivWidth: A software tool for the calculation of river widths from remotely sensed imagery","volume":"5","author":"Pavelsky","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"113452","DOI":"10.1016\/j.rse.2023.113452","article-title":"Semantic segmentation of water bodies in very high-resolution satellite and aerial images","volume":"287","author":"Wieland","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e2023EA002845","DOI":"10.1029\/2023EA002845","article-title":"Exploring the Influence of Input Feature Space on CNN-Based Geomorphic Feature Extraction From Digital Terrain Data","volume":"10","author":"Maxwell","year":"2023","journal-title":"Earth Space Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1002\/esp.5305","article-title":"Comparing geomorphological maps made manually and by deep learning","volume":"47","author":"Meijles","year":"2022","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"112107","DOI":"10.1016\/j.rse.2020.112107","article-title":"Adopting deep learning methods for airborne RGB fluvial scene classification","volume":"251","author":"Carbonneau","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e2020WR027608","DOI":"10.1029\/2020WR027608","article-title":"Using Deep Learning for Automatic Water Stage Measurements","volume":"57","author":"Eltner","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"349","DOI":"10.5194\/esurf-10-349-2022","article-title":"Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset","volume":"10","author":"Chen","year":"2022","journal-title":"Earth Surf. Dyn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.5194\/hess-25-2567-2021","article-title":"GRAINet: Mapping grain size distributions in river beds from UAV images with convolutional neural networks","volume":"25","author":"Lang","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1142\/S1793351X20400036","article-title":"Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks","volume":"14","author":"Pai","year":"2020","journal-title":"Int. J. Semant. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104805","DOI":"10.1016\/j.cageo.2021.104805","article-title":"DeepRivWidth: Deep learning based semantic segmentation approach for river identification and width measurement in SAR images of Coastal Karnataka","volume":"154","author":"Verma","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7750281","DOI":"10.1155\/2022\/7750281","article-title":"River Segmentation of Remote Sensing Images Based on Composite Attention Network","volume":"2022","author":"Fan","year":"2022","journal-title":"Complexity"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"557","DOI":"10.5194\/esurf-5-557-2017","article-title":"Deriving principal channel metrics from bank and long-profile geometry with the R package cmgo","volume":"5","author":"Golly","year":"2017","journal-title":"Earth Surf. Dyn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/LGRS.2019.2920225","article-title":"RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery","volume":"17","author":"Yang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","first-page":"1500505","article-title":"GrabRiver: Graph-Theory-Based River Width Extraction From Remote Sensing Imagery","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Refaeilzadeh, P., Tang, L., and Liu, H. (2016). Cross-Validation. Encyclopedia of Database Systems, Springer Science+BusinessMedia.","DOI":"10.1007\/978-1-4899-7993-3_565-2"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017, January 22\u201329). Revisiting unreasonable effectiveness of data in deep learning era. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MIS.2009.36","article-title":"The Unreasonable Effectiveness of Data","volume":"24","author":"Halevy","year":"2009","journal-title":"IEEE Intell. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"237428951987308","DOI":"10.1177\/2374289519873088","article-title":"Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods","volume":"6","author":"Rashidi","year":"2019","journal-title":"Acad. Pathol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1002\/esp.4515","article-title":"What controls submarine channel development and the morphology of deltas entering deep-water fjords?","volume":"44","author":"Gales","year":"2018","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_66","first-page":"833","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"11211","author":"Chen","year":"2018","journal-title":"Comput. Vis. Eccv"},{"key":"ref_67","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_69","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.gmod.2014.03.007","article-title":"Computing a compact spline representation of the medial axis transform of a 2D shape","volume":"76","author":"Zhu","year":"2014","journal-title":"Graph. Models"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Fernandez-Moral, E., Martins, R., Wolf, D., and Rives, P. (2018, January 26\u201330). A New Metric for Evaluating Semantic Segmentation: Leveraging Global and Contour Accuracy. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500497"},{"key":"ref_74","first-page":"48","article-title":"Hazard and risk from large landslides from Mount Meager volcano, British Columbia, Canada","volume":"2","author":"Friele","year":"2008","journal-title":"Georisk: Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1130\/GES01389.1","article-title":"Rheological evolution of the Mount Meager 2010 debris avalanche, southwestern British Columbia","volume":"13","author":"Roberti","year":"2017","journal-title":"Geosphere"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.geomorph.2014.04.002","article-title":"Geomorphic response of Lillooet River, British Columbia, to meander cutoffs and base level lowering","volume":"217","author":"Weatherly","year":"2014","journal-title":"Geomorphology"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"W11502","DOI":"10.1029\/2005WR004365","article-title":"Automated Thiessen polygon generation","volume":"42","author":"Han","year":"2006","journal-title":"Water Resour. Res."},{"key":"ref_78","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Med. Image Comput. Comput.-Assist. Interv. Pt Iii"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6272","DOI":"10.1038\/s41467-021-26565-5","article-title":"A deep learning approach for complex microstructure inference","volume":"12","author":"Durmaz","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 Ieee Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Gurnell, A.M., Peiry, J.-L., and Petts, G.E. (2003). Using Historical Data in Fluvial Geomorphology. Tools in Fluvial Geomorphology, John Wiley & Sons.","DOI":"10.1002\/0470868333.ch4"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1002\/esp.5560","article-title":"New remote method to systematically extract bedrock channel width of small catchments across large spatial scales using high-resolution digital elevation models","volume":"48","author":"Eidmann","year":"2023","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1002\/2014WR016806","article-title":"Morphodynamic response of a variable-width channel to changes in sediment supply","volume":"51","author":"Nelson","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1029\/2017JF004405","article-title":"The Dynamics of Channel Slope, Width, and Sediment in Actively Eroding Bedrock River Systems","volume":"123","author":"Yanites","year":"2018","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.5194\/gmd-15-2917-2022","article-title":"Modeling of streamflow in a 30\u2009km long reach spanning 5 years using OpenFOAM 5.x","volume":"15","author":"Chen","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.5194\/esurf-10-1253-2022","article-title":"A combined approach of experimental and numerical modeling for 3D hydraulic features of a step-pool unit","volume":"10","author":"Zhang","year":"2022","journal-title":"Earth Surf. Dyn."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.gmod.2005.01.002","article-title":"The \u201c\u03bb-medial axis\u201d","volume":"67","author":"Chazal","year":"2005","journal-title":"Graph. Models"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jhydrol.2007.03.021","article-title":"Field-derived relationships for flow velocity and resistance in high-gradient streams","volume":"340","author":"Comiti","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"F02008","DOI":"10.1029\/2007JF000831","article-title":"Is the critical Shields stress for incipient sediment motion dependent on channel-bed slope?","volume":"113","author":"Lamb","year":"2008","journal-title":"J. Geophys. Res.-Earth Surf."},{"key":"ref_91","unstructured":"Meyer-Peter, E., and M\u00fcller, R. (1948). Formulas for Bed-Load Transport, International Association for Hydro-Environment Engineering and Research."},{"key":"ref_92","first-page":"W05412","article-title":"Feedback between bed load transport and flow resistance in gravel and cobble bed rivers","volume":"44","author":"Recking","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/BF02162161","article-title":"Smoothing by Spline Functions","volume":"10","author":"Reinsch","year":"1967","journal-title":"Numer. Math."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.0033-0124.1987.00189.x","article-title":"Stream power terminology","volume":"39","author":"Rhoads","year":"1987","journal-title":"Prof. Geogr."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/2017WR021376","article-title":"Variability of Bed Load Transport During Six Summers of Continuous Measurements in Two Austrian Mountain Streams (Fischbach and Ruetz)","volume":"54","author":"Rickenmann","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"W09536","DOI":"10.1029\/2009WR007913","article-title":"Flow resistance in steep streams: An experimental study","volume":"46","author":"Zimmermann","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Li, W.Q., Hassan, M.A., and Zhang, C.D. (2024). A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width. figshare Dataset.","DOI":"10.3390\/rs16234614"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4614\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:50:47Z","timestamp":1760115047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":97,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234614"],"URL":"https:\/\/doi.org\/10.3390\/rs16234614","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]}}}