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The developed GDSLO algorithm is newly devised by the incorporation of Stochastic Gradient Descent (SGD) and Sea Lion Optimization Algorithm (SLnO) algorithm. Input image is pre-processed and U-Net is employed in road segmentation phase for extracting the road surfaces. Meanwhile, training data of U-Net has to be done by using the GDSLO optimization algorithm. Once road segmentation is done, road edge detection and road centerline detection is performed using Fully Convolutional Network (FCN). However, the developed GDSLO-based U-Net method achieved superior performance by containing the estimation criteria, including precision, recall, and F1-measure through highest rate of 0.887, 0.930, and 0.809, respectively.<\/jats:p>","DOI":"10.1142\/s0219467823500584","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T08:58:43Z","timestamp":1662022723000},"source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning-Enabled Road Segmentation and Edge-Centerline Extraction from High-Resolution Remote Sensing Images"],"prefix":"10.1142","volume":"23","author":[{"given":"Miral Jerambhai","family":"Patel","sequence":"first","affiliation":[{"name":"Atmiya University, Rajkot, Gujarat, India"},{"name":"Government Engineering College, Rajkot, Gujarat, India"}]},{"given":"Ashish M.","family":"Kothari","sequence":"additional","affiliation":[{"name":"Department of Electronics & Communication Engineering, Atmiya University, Rajkot, Gujarat, India"}]}],"member":"219","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"S0219467823500584BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2870871"},{"key":"S0219467823500584BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.036"},{"key":"S0219467823500584BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2669341"},{"key":"S0219467823500584BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/WHISPERS52202.2021.9484007"},{"key":"S0219467823500584BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70087-8_70"},{"key":"S0219467823500584BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2991733"},{"key":"S0219467823500584BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3016086"},{"key":"S0219467823500584BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3039534"},{"key":"S0219467823500584BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3023549"},{"key":"S0219467823500584BIB010","doi-asserted-by":"publisher","DOI":"10.3390\/s20072064"},{"key":"S0219467823500584BIB011","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi5070125"},{"key":"S0219467823500584BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2409192"},{"issue":"2","key":"S0219467823500584BIB013","first-page":"32","volume":"3","author":"Kaur S.","year":"2013","journal-title":"Int. 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