{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:43:04Z","timestamp":1774611784740,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T00:00:00Z","timestamp":1588982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes a deep convolutional neural network model with encoder-decoder architecture to extract road network from satellite images. We employ ResNet-18 and Atrous Spatial Pyramid Pooling technique to trade off between the extraction precision and running time. A modified cross entropy loss function is proposed to train our deep model. A PointRend algorithm is used to recover a smooth, clear and sharp road boundary. The augmentated DeepGlobe dataset is used to train our deep model and the asynchronous training method is applied to accelerate the training process. Five salellite images covering Xiaomu village are taken as input to evaluate our model. The proposed E-Road model has fewer number of parameters and shorter training time. The experiments show E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give the accurate predictions for the images with complex environment.<\/jats:p>","DOI":"10.3390\/e22050535","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T10:01:18Z","timestamp":1589191278000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-2553","authenticated-orcid":false,"given":"Bowei","family":"Shan","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., and Yuille, A.L. (2016, January 27\u201330). Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.492"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., and Yuille, A.L. (2016, January 27\u201330). Attention to scale: Scale-aware semantic image segmentation. Proceedings of the IEEE Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.396"},{"key":"ref_7","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., and DeWitt, D. (2018, January 18\u201322). RoadTracer: Automatic Extraction of Road Networks From Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00496"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Luo, W., and Urtasun, R. (2017, January 22\u201329). Deeproadmapper:Extracting road topology from aerial images. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.372"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G.E. (2010, January 5\u201311). Learning to detect roads in high-resolution aerial images. Proceedings of the ECCV, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_13","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1109\/TGRS.2018.2870871","article-title":"RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes From High-Resolution Remotely Sensed Images","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gao, L., Song, W., Dai, J., and Chen, Y. (2019). Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11050552"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., 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_17","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Artacho, B., and Savakis, A. (2019). Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation. Sensors, 19.","DOI":"10.3390\/s19245361"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S.I., Hughes, F., Tuia, D., and Raska, R. (2018, January 18\u201322). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial image","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2019). PointRend: Image Segmentation as Rendering. arXiv.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbelaez, P., Girshick, R., and Malik, J. (2015, January 7\u201312). Hypercolumns for object segmentation and fine-grained localization. Proceedings of the CVPR, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_24","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/5\/535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:27:19Z","timestamp":1760174839000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/5\/535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,9]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["e22050535"],"URL":"https:\/\/doi.org\/10.3390\/e22050535","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,9]]}}}