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Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
M. Song and D. Civco, “Road extraction using SVM and image segmentation,” PE&RS , vol. 70, no. 12, pp. 1365–1371, 2004
2004
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2005
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X. Huang and L. Zhang, “Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines,” IJRS , vol. 30, no. 8, pp. 1977–1987, 2009
2009
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V. Mnih and G. Hinton, “Learning to detect roads in high-resolution aerial images,” ECCV , pp. 210–223, 2010
2010
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S. Das, T. T. Mirnalinee, and K. Varghese, “Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images,” TGRS , vol. 49, no. 10, pp. 3906–3931, 2011
2011
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C. Unsalan and B. Sirmacek, “Road network detection using probabilistic and graph theoretical methods,” TGRS , vol. 50, no. 11, pp. 4441–4453, 2012
2012
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V. Mnih and G. E. Hinton, “Learning to label aerial images from noisy data,” in ICML , 2012, pp. 567–574
2012
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B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in NIPS , 2014, pp. 487–495
2014
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2014
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G. Cheng, Y. Wang, Y. Gong, F. Zhu, and C. Pan, “Urban road extraction via graph cuts based probability propagation,” in ICIP , 2015, pp. 5072–5076
2015
Cited alongside, same era.
B. Liu, H. Wu, Y. Wang, and W. Liu, “Main road extraction from ZY-3 grayscale imagery based on directional mathematical morphology and VGI prior knowledge in urban areas,” PLOS ONE , vol. 10, no. 9, p. e0138071, 2015
2015
Cited alongside, same era.
C. Sujatha and D. Selvathi, “Connected component-based technique for automatic extraction of road centerline in high resolution satellite images,” J. Image Video Process. , vol. 2015, no. 1, p. 8, 2015
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in CVPR , 2015, pp. 1–9
2015
Cited alongside, same era.
Q. Zhang, Y. Wang, Q. Liu, X. Liu, and W. Wang, “CNN based suburban building detection using monocular high resolution google earth images,” in IGARSS , 2016, pp. 661–664
2016
Later among the works it cites.
L. Zhang, L. Zhang, and B. Du, “Deep learning for remote sensing data: A technical tutorial on the state of the art,” Geosci. Remote Sens. Mag. , vol. 4, no. 2, pp. 22–40, 2016
2016
Later among the works it cites.
Z. Zhang, Y. Wang, Q. Liu, L. Li, and P. Wang, “A CNN based functional zone classification method for aerial images,” in IGARSS , 2016, pp. 5449–5452
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Later among the works it cites.
——, “Identity mappings in deep residual networks,” in ECCV , 2016, pp. 630–645
2016
Later among the works it cites.
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015, pp. 3431–3440
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015, pp. 234–241
2015
Cited alongside, same era.
F. Chollet et al. , “Keras,” https://github.com/fchollet/keras
2015
Cited alongside, same era.
S. Saito, T. Yamashita, and Y. Aoki, “Multiple object extraction from aerial imagery with convolutional neural networks,” J. ELECTRON IMAGING , vol. 2016, no. 10, pp. 1–9, 2016
2016
Cited alongside, same era.
G. Cheng, F. Zhu, S. Xiang, and C. Pan, “Road centerline extraction via semisupervised segmentation and multidirection nonmaximum suppression,” GRSL , vol. 13, no. 4, pp. 545–549, 2016
2016
Cited alongside, same era.
R. Alshehhi and P. R. Marpu, “Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images,” P&RS , vol. 126, pp. 245–260, 2017
2017
Closest in time.
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,” TGRS , vol. 55, no. 6, pp. 3322–3337, 2017
2017
Closest in time.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” TPAMI , vol. 39, no. 6, p. 1137, 2017
2017
Closest in time.