{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:47:30Z","timestamp":1778860050547,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T00:00:00Z","timestamp":1698451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)","award":["NRF-2023R1A2C1007742"],"award-info":[{"award-number":["NRF-2023R1A2C1007742"]}]},{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)","award":["RSP-2023\/14"],"award-info":[{"award-number":["RSP-2023\/14"]}]},{"name":"University of Technology Sydney","award":["NRF-2023R1A2C1007742"],"award-info":[{"award-number":["NRF-2023R1A2C1007742"]}]},{"name":"University of Technology Sydney","award":["RSP-2023\/14"],"award-info":[{"award-number":["RSP-2023\/14"]}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education of the Republic of Korea and the National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2023R1A2C1007742"],"award-info":[{"award-number":["NRF-2023R1A2C1007742"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education of the Republic of Korea and the National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RSP-2023\/14"],"award-info":[{"award-number":["RSP-2023\/14"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"King Saud University","award":["NRF-2023R1A2C1007742"],"award-info":[{"award-number":["NRF-2023R1A2C1007742"]}]},{"name":"King Saud University","award":["RSP-2023\/14"],"award-info":[{"award-number":["RSP-2023\/14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network\u2019s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.<\/jats:p>","DOI":"10.3390\/s23218783","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:26:55Z","timestamp":1698672415000},"page":"8783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6027-3914","authenticated-orcid":false,"given":"Mohd Jawed","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar 783370, Assam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4079-4485","authenticated-orcid":false,"given":"Pankaj Pratap","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar 783370, Assam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney 2007, Australia"},{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-3225","authenticated-orcid":false,"given":"Chang-Wook","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Science Education, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si 24341, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,28]]},"reference":[{"key":"ref_1","first-page":"103159","article-title":"A comparative study of loss functions for road segmentation in remotely sensed road datasets","volume":"116","author":"Xu","year":"2023","journal-title":"Int. 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