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Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms.
Y.-Y. Chiang and C. A. Knoblock, “Extracting road vector data from raster maps,” in International Workshop on Graphics Recognition . Springer, 2009, pp. 93–105
2009
Earlier work this paper cites.
V. Mnih and G. E. Hinton, “Learning to detect roads in high-resolution aerial images,” in European Conference on Computer Vision . Springer, 2010, pp. 210–223
2010
Earlier work this paper cites.
S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2010, pp. 661–668
2010
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635
2011
Earlier work this paper cites.
C. Unsalan and B. Sirmacek, “Road network detection using probabilistic and graph theoretical methods,” IEEE Transactions on Geoscience and Remote Sensing , vol. 50, no. 11, pp. 4441–4453, 2012
2012
Earlier work this paper cites.
W. Shi, Z. Miao, Q. Wang, and H. Zhang, “Spectral–spatial classification and shape features for urban road centerline extraction,” IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 4, pp. 788–792, 2013
2013
Earlier work this paper cites.
X. Hu, Y. Li, J. Shan, J. Zhang, and Y. Zhang, “Road centerline extraction in complex urban scenes from lidar data based on multiple features,” IEEE Transactions on Geoscience and Remote Sensing , vol. 52, no. 11, pp. 7448–7456, 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
G. Cheng, F. Zhu, S. Xiang, and C. Pan, “Road centerline extraction via semisupervised segmentation and multidirection nonmaximum suppression,” IEEE Geoscience and Remote Sensing Letters , vol. 13, no. 4, pp. 545–549, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
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
Earlier work this paper cites.
G. Cheng, Y. Wang, S. Xu, H. Wang, S. Xiang, and C. Pan, “Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 6, pp. 3322–3337, 2017
2017
Earlier work this paper cites.
D. Bulatov, S. Wenzel, G. Häufel, and J. Meidow, “Chain-wise generalization of road networks using model selection,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 4, p. 59, 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 801–818
2018
Earlier work this paper cites.
N. Homayounfar, W.-C. Ma, S. Kowshika Lakshmikanth, and R. Urtasun, “Hierarchical recurrent attention networks for structured online maps,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3417–3426
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, A. Ku, and D. Tran, “Image transformer,” in International Conference on Machine Learning . PMLR, 2018, pp. 4055–4064
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. V. Etten, “City-scale road extraction from satellite imagery v2: Road speeds and travel times,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 1786–1795
2020
Later among the works it cites.
S. He, F. Bastani, S. Jagwani, M. Alizadeh, H. Balakrishnan, S. Chawla, M. M. Elshrif, S. Madden, and M. A. Sadeghi, “Sat2graph: road graph extraction through graph-tensor encoding,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16 . Springer, 2020, pp. 51–67
2020
Later among the works it cites.
Y.-Q. Tan, S.-H. Gao, X.-Y. Li, M.-M. Cheng, and B. Ren, “Vecroad: Point-based iterative graph exploration for road graphs extraction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8910–8918
2020
Later among the works it cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2018
Cited alongside, same era.
N. O. Department of Information Technology & Telecommunications (DoITT), “NYC-Planimetrics Database,” https://github.com/CityOfNewYork/nyc-planimetrics , 2019
2019
Cited alongside, same era.
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 Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 385–10 393
2019
Cited alongside, same era.
Z. Li, J. D. Wegner, and A. Lucchi, “Topological map extraction from overhead images,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1715–1724
2019
Cited alongside, same era.
D. Belli and T. Kipf, “Image-conditioned graph generation for road network extraction,” NeurIPS 2019 workshop on Graph Representation Learning , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Kang, S. Gao, and R. E. Roth, “Transferring multiscale map styles using generative adversarial networks,” International Journal of Cartography , vol. 5, no. 2-3, pp. 115–141, 2019
2019
Cited alongside, same era.
S. Wenzel and D. Bulatov, “Simultaneous chain-forming and generalization of road networks,” Photogrammetric Engineering & Remote Sensing , vol. 85, no. 1, pp. 19–28, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
R. He, X. Li, G. Chen, G. Chen, and Y. Liu, “Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries,” Expert Systems with Applications , vol. 150, p. 113244, 2020
2020
Later among the works it cites.
J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang et al. , “Deep high-resolution representation learning for visual recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 10, pp. 3349–3364, 2020
2020
Later among the works it cites.
W. Gedara Chaminda Bandara, J. M. J. Valanarasu, and V. M. Patel, “Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving,” arXiv e-prints , pp. arXiv–2109, 2021
2021
Later among the works it cites.
G. Zhou, W. Chen, Q. Gui, X. Li, and L. Wang, “Split depth-wise separable graph-convolution network for road extraction in complex environments from high-resolution remote-sensing images,” IEEE Transactions on Geoscience and Remote Sensing , 2021
2021
Later among the works it cites.
Z. Xu, Y. Sun, and M. Liu, “Topo-boundary: A benchmark dataset on topological road-boundary detection using aerial images for autonomous driving,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7248–7255, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Q. Li, Y. Wang, Y. Wang, and H. Zhao, “Hdmapnet: A local semantic map learning and evaluation framework,” 2021
2021
Later among the works it cites.
Y. Zhou, Y. Takeda, M. Tomizuka, and W. Zhan, “Automatic construction of lane-level hd maps for urban scenes,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 6649–6656
2021
Later among the works it cites.
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
Later among the works it cites.
Z. Xu, Y. Sun, L. Wang, and M. Liu, “Cp-loss: Connectivity-preserving loss for road curb detection in autonomous driving with aerial images,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1117–1123
2021
Later among the works it cites.
Y. Xu, W. Xu, D. Cheung, and Z. Tu, “Line segment detection using transformers without edges,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4257–4266
2021
Later among the works it cites.
Y. B. Can, A. Liniger, D. P. Paudel, and L. Van Gool, “Structured bird’s-eye-view traffic scene understanding from onboard images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 661–15 670
2021
Later among the works it cites.
S. He and H. Balakrishnan, “Lane-level street map extraction from aerial imagery,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 2080–2089
2089
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