1. [1] Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T., Eklund, P. "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. [DOI:10.1016/j.jtte.2016.05.005. https://doi.org/10.1016/j.jtte.2016.05.005]

2. [2] Huang, X., Zhang, L. "Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines," International Journal of Remote Sensing, Vol. 30, No.8, pp. 1977-1987, DOI: 10.1080/01431160802546837. [DOI:10.1080/01431160802546837]

3. [3] Bicego, M., Dalfini, S., Vernazza G., Murino, V. "Automatic road extraction from aerial images by probabilistic contour tracking," Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), Barcelona, Spain, 2003, pp. III-585, doi: 10.1109/ICIP.2003.1247312. [DOI:10.1109/ICIP.2003.1247312]

4. [4] Baumgartner, A., Steger, C., Mayer, H., Eckstein, W., Ebner, H. "AUTOMATIC ROAD EXTRACTION BASED ON MULTI-SCALE, GROUPING, AND CONTEXT." Photogrammetric Engineering and Remote Sensing, Vol. 65, No.1999, pp. 777-785. 1999.

5. [5] Xu, Y., Xie, Z., Feng, Y., Chen, Z. "Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning." Remote Sensing, Vol. 10, No.9, pp.1461, 2018. DOI:10.3390/rs10091461. [DOI:10.3390/rs10091461]

6. [6] Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., Alamri, A. "Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review," Remote Sens. Vol.12, No.1444, 2020. doi:10.3390/rs12091444. [DOI:10.3390/rs12091444]

7. [7] Choi, S., Do, M. "Development of the Road Pavement Deterioration Model Based on the Deep Learning Method," Electronics 2020, 9, 3; doi:10.3390/electronics9010003. [DOI:10.3390/electronics9010003]

8. [8] Mnih, V., Hinton, G.E. "Learning to Detect Roads in High-Resolution Aerial Images," K. Daniilidis, P. Maragos, N. Paragios (Eds.): ECCV 2010, Part VI, LNCS 6316, pp. 210-223, 2010. [DOI:10.1007/978-3-642-15567-3_16]

9. [9] Li, P., Zang, Y.,Wang, C., Li, J., Cheng, M., Luo, L., Yu, Y. "ROAD NETWORK EXTRACTION VIA DEEP LEARNING AND LINE INTEGRAL CONVOLUTION," 978-1-5090-3332-4/16/$31.00 ©2016 IEEE.

10. [10] Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., DeWitt, D. "RoadTracer: Automatic Extraction of Road Networks from Aerial Images," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. [DOI:10.1109/CVPR.2018.00496]

11. [11] Yoo., H.J. "Deep Convolution Neural Networks in Computer Vision: a Review," IEIE Transactions on Smart Processing and Computing, 4(1), 2015 http://dx.doi.org/10.5573/IEIESPC.2015.4.1.035. [DOI:10.5573/IEIESPC.2015.4.1.035]

12. [12] Garcia-Garcia, A., Orts-Escolano, S., Oprea, S.O., Villena-Martinez, V., Garcia-Rodriguez, J. "A Review on Deep Learning Techniques Applied to Semantic Segmentation," Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI), [DOI:10.48550/arXiv.1704.06857.]

13. [13] Ronneberger, O., Fischer, Ph., Brox, Th. "U-Net: Convolutional Networks for Biomedical Image Segmentation," N. Navab et al. (Eds.): MICCAI 2015, Part III, LNCS 9351, pp. 234-241, 2015. DOI: 10.1007/978-3-319-24574-4_28. [DOI:10.1007/978-3-319-24574-4_28]

14. [14] Pal, K., Yadav, P., Katal, N. "RoadSegNet: a deep learning framework for autonomous urban road detection," Journal of Engineering and Applied Science, 2022. [DOI:10.1186/s44147-022-00162-9. https://doi.org/10.1186/s44147-022-00162-9]

15. [15] Safavi, S.H., Sadeghi, M., Ebadpour, M. "DPRSMR: Deep learning-based Persian Road Surface Marking Recognition," J. Electr. Comput. Eng. Innovations, Vol. 11, No.2, PP. 409-418, 2023. Doi: 10.22061/jecei.2023.9496.627.

16. [16] Shao, Z., Tang, P., Wang, Zh., Saleem, N., Yam, S., Sommai, Ch. "BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images," Remote Sens. 2020, 12, 1050; doi:10.3390/rs12061050. [DOI:10.3390/rs12061050]

17. [17] Long, J., Shelhamer, E., Darrell, T. "Fully convolutional networks for semantic segmentation," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015 pp. 3431-3440. doi: 10.1109/CVPR.2015.7298965. [DOI:10.1109/CVPR.2015.7298965]

18. [18] Alokasi, H., Ahmad, M.B. "Deep Learning-Based Frameworks for Semantic Segmentation of Road Scenes." Electronics 2022, 11, 1884. [DOI:10.3390/electronics11121884. https://doi.org/10.3390/electronics11121884]

19. [19] Lin, G., Milan, A., Shen, Ch., Reid, J. "RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition, DOI 10.1109/CVPR.2017.549. [DOI:10.1109/CVPR.2017.549]

20. [20] Pan, X., Yang, F., Gao, L., Chen, Zh., Zhang, B., Fan, H., Ren, J. "Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms," Remote Sens. 2019, 11, 917; doi:10.3390/rs11080917. [DOI:10.3390/rs11080917]

21. [21] Xu, Y., Wu, L., Xie, Z., Chen, Z. 2018. "Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters," Remote Sens. 2018, 10, 144; doi: 10.3390/rs10010144. [DOI:10.3390/rs10010144]

22. [22] Wang, S., Hou, X., Zhao, X. "Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block," in IEEE Access, vol. 8, pp. 7313-7322, 2020, doi: 10.1109/ACCESS.2020.2964043. [DOI:10.1109/ACCESS.2020.2964043]

23. [23] Wei, S., Ji, S., Lu, M. 2020. "Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 3, pp. 2178-2189, March 2020, doi: 10.1109/TGRS.2019.2954461. [DOI:10.1109/TGRS.2019.2954461]

24. [24] Kumar, R., Amitab, K. 2023. "Semantic Segmentation of Road Scene Using Deep Learning," In: Basu, S., Kole, D.K., Maji, A.K., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Lecture Notes in Networks and Systems, vol 404. Springer, Singapore. [DOI:10.1007/978-981-19-0105-8_13. https://doi.org/10.1007/978-981-19-0105-8_13]

25. [25] Yang, H. L., Yuan, J., Lunga, D., Laverdiere, M., Rose, A., Bhaduri, B. "Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 8, pp. 2600-2614, Aug. 2018, doi: 10.1109/JSTARS.2018.2835377. [DOI:10.1109/JSTARS.2018.2835377]

26. [26] Shao, S., Xiao, L., Lin, L., Ren, C., Tian, J. "Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery," Remote Sens. 2022, 14, 2061. [DOI:10.3390/rs14092061. https://doi.org/10.3390/rs14092061]

27. [27] Hou, Y., Liu, Z., Zhang, T., Li, Y. 2021. " C-UNet: Complement UNet for Remote Sensing Road Extraction," Sensors 2021, 21, 2153. [DOI:10.3390/s21062153. https://doi.org/10.3390/s21062153]

28. [28] Zhang, Z., Liu, Q., Wang, Y. 2018. " Road Extraction by Deep Residual U-Net," IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. [DOI:10.1109/LGRS.2018.2802944]

29. [29] Zhou, L., Zhang, C., Wu, M. "D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction," in Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, USA, Jun. 2018, pp. 192-1924. [DOI:10.1109/CVPRW.2018.00034]

30. [30] Yu, F., Koltun, V. 2016. " MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS," Published as a conference paper at ICLR 2016

31. [31] Chen, L.C., Papandreou, G., Schroff, F., Adam, H. 2017. "Rethinking Atrous Convolution for Semantic Image Segmentation," Computer Vision and Pattern Recognition, 2017. [DOI:10.48550/arXiv.1706.05587.]

32. [32] Khan, T.M., Arsalan, M., Iqbal, Sh., Razzak, I., Meijering, E. 2023. "Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation." Doi: 10.13140/RG.2.2.14160.48642. [DOI:10.1109/DICTA60407.2023.00030]

33. [33] Zhong, Z., Li, J., Cui, W., Jiang, H. 2016. "Fully CONVOLUTIONAL NETWORKS FOR BUILDING AND ROAD EXTRACTION: PRELIMINARY RESULTS," https://ieeexplore.ieee.org/document/7729406. [DOI:10.1109/IGARSS.2016.7729406]

34. [34] Kingma, D., Ba, J. "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations. 2014.

35. [35] Mishra, P., Sarawadekar, K. 2019. "Polynomial Learning Rate Policy with Warm Restart for Deep Neural Network," TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), Kochi, India, pp. 2087-2092, doi: 10.1109/TENCON.2019.8929465. [DOI:10.1109/TENCON.2019.8929465]