{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:41:47Z","timestamp":1782409307845,"version":"3.54.5"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T00:00:00Z","timestamp":1569196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 \u00d7 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 \u00d7 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.<\/jats:p>","DOI":"10.3390\/s19194115","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T11:02:00Z","timestamp":1569236520000},"page":"4115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks"],"prefix":"10.3390","volume":"19","author":[{"given":"Yuxia","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9875-9853","authenticated-orcid":false,"given":"Lei","family":"He","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunlong","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1080\/01431161.2017.1294779","article-title":"Fast and robust geometric correction for mosaicking UAV images with narrow overlaps","volume":"38","author":"Kim","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","first-page":"69","article-title":"Road extraction method of full convolution neural network remote sensing image","volume":"33","author":"Liu","year":"2018","journal-title":"Remote Sens. Inf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2018.10.036","article-title":"Multiscale road centerlines extraction from high-resolution aerial imagery","volume":"329","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z. (2018). Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1080\/01431161.2015.1054049","article-title":"Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sun, T., Chen, Z., Yang, W., and Wang, Y. (2018, January 18\u201322). Stacked u-nets with multi-output for road extraction. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00033"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"016020","DOI":"10.1117\/1.JRS.12.016020","article-title":"UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle","volume":"12","author":"Kestur","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","first-page":"423","article-title":"A novel road segmentation technique from orthophotos using deep convolutional autoencoders","volume":"33","author":"Sameen","year":"2017","journal-title":"Korean J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Costea, D., Marcu, A., Slusanschi, E., and Leordeanu, M. (2018, January 18\u201322). Roadmap Generation using a Multi-Stage Ensemble of Deep Neural Networks with Smoothing-Based Optimization. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00038"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Filin, O., Zapara, A., and Panchenko, S. (2018, January 18\u201322). Road detection with EOSResUNet and post vectorizing algorithm. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00036"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Seferbekov, S.S., Iglovikov, V., and Shvets, A. (2018, January 18\u201322). Fully convolutional network for automatic road extraction from satellite imagery. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00035"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_15","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_17","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_18","unstructured":"Wiedemann, C., Heipke, C., Mayer, H., and Jamet, O. (1998). Empirical evaluation of automatically extracted road axes. Empirical Evaluation Techniques in Computer Vision, IEEE Computer Society Press."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, P., Ni, B., Geng, C., Hu, J., and Xu, Y. (2018, January 18\u201322). Scale-transferrable object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00062"},{"key":"ref_20","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3584","DOI":"10.1080\/01431161.2016.1201227","article-title":"A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data","volume":"37","author":"Perciano","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"217","article-title":"Region-based urban road extraction from VHR satellite images using Binary Partition Tree","volume":"44","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shen, Z., Liu, Z., Li, J., Jiang, Y.G., Chen, Y., and Xue, X. (2017, January 22\u201329). Dsod: Learning deeply supervised object detectors from scratch. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.212"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:20Z","timestamp":1760189000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,23]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194115"],"URL":"https:\/\/doi.org\/10.3390\/s19194115","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,23]]}}}