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Road extraction is an essential step in building autonomous navigation systems.
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C. C. T. Mendes, V. Frémont, and D. F. Wolf, “Exploiting fully convolutional neural networks for fast road detection,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 3174–3179
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2016
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2016
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I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, and R. Raskar, “Deepglobe 2018: A challenge to parse the earth through satellite images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 172–181
2018
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A. Kendall, Y. Gal, and R. Cipolla, “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7482–7491
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W. Wang, N. Yang, Y. Zhang, F. Wang, T. Cao, and P. Eklund, “A review of road extraction from remote sensing images,” Journal of Traffic and Transportation Engineering (English Edition) , vol. 3, no. 3, pp. 271–282, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2095756416301076
2016
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A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 483–499
2016
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G. Máttyus, W. Luo, and R. Urtasun, “Deeproadmapper: Extracting road topology from aerial images,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3438–3446
2017
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A. Chaurasia and E. Culurciello, “Linknet: Exploiting encoder representations for efficient semantic segmentation,” in 2017 IEEE Visual Communications and Image Processing (VCIP) . IEEE, 2017, pp. 1–4
2017
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2017
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V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 12, pp. 2481–2495, 2017
2017
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A. Van Etten, “Spacenet road detection and routing challenge-part i,” 2017
2017
Cited alongside, same era.
F. Bastani, S. He, S. Abbar, M. Alizadeh, H. Balakrishnan, S. Chawla, S. Madden, and D. DeWitt, “Roadtracer: Automatic extraction of road networks from aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4720–4728
2018
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2018
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Y. Chen, M. Rohrbach, Z. Yan, Y. Shuicheng, J. Feng, and Y. Kalantidis, “Graph-based global reasoning networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 433–442
2019
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A. Batra, S. Singh, G. Pang, S. Basu, C. Jawahar, and M. Paluri, “Improved road connectivity by joint learning of orientation and segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 385–10 393
2019
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A. Araujo, W. Norris, and J. Sim, “Computing receptive fields of convolutional neural networks,” Distill , 2019, https://distill.pub/2019/computing-receptive-fields
2019
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2019
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A. Wulamu, Z. Shi, D. Zhang, and Z. He, “Multiscale road extraction in remote sensing images,” Computational intelligence and neuroscience , vol. 2019, 2019
2019
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2020
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A. Abdollahi, B. Pradhan, N. Shukla, S. Chakraborty, and A. Alamri, “Deep learning approaches applied to remote sensing datasets for road extraction: A state-of-the-art review,” Remote Sensing , vol. 12, no. 9, p. 1444, 2020
2020
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X. Li, Y. Yang, Q. Zhao, T. Shen, Z. Lin, and H. Liu, “Spatial pyramid based graph reasoning for semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8950–8959
2020
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Z. Xu, Y. Sun, and M. Liu, “icurb: Imitation learning-based detection of road curbs using aerial images for autonomous driving,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1097–1104, 2021
2021
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