Expanded U-Net Model for Road Extraction from Satellite Images

[1] World health organization (2020). https://www.who.int/news-room/fact-sheets/detail/road traffic-injuries

[2] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR. (2015)

[3] Majidi, N., Kiani, K., Rastgoo, R.: A deep model for super-resolution enhancement from a single image. Journal of AI and Data Mining, vol. 8, no. 4, pp. 451-460, 2020.

[4] Rastgoo, R., Sattari Naeini, V.: A neurofuzzy QoS-aware routing protocol for smart grids. 2014 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 1080-1084, 2014.

[5] Kiani, K., Hematpour, R., Rastgoo, R.: Automatic grayscale image colorization using a deep hybrid model. Journal of AI and Data Mining, vol. 9, no. 3, pp. 321-328, 2021.

[6] Rastgoo, R., Kiani, K., Escalera, S., Sabokrou, M.: Multi-modal zero-shot dynamic hand gesture recognition. Expert Systems with Applications, vol. 247, pp. 123349, 2024.

[7] Rastgoo, R., Kiani, K.: Face recognition using fine-tuning of Deep Convolutional Neural Network and transfer learning. Journal of Modeling in Engineering, vol. 17, no. 58, pp. 103-111, 2019.

[8] Rastgoo, R., Sattari-Naeini, V.: Tuning parameters of the QoS-aware routing protocol for smart grids using genetic algorithm. Applied Artificial Intelligence, vol. 30, no. 1, pp. 52-76, 2016.

[9] Rastgoo, R., Sattari-Naeini, V.: Gsomcr: Multi-constraint genetic-optimized qos-aware routing protocol for smart grids. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 42, pp. 185-194, 2018.

[10] Zarbafi, S., Kiani, K., Rastgoo, R.: Spoken Persian digits recognition using deep learning. Journal of Modeling in Engineering, vol. 21, no. 74, pp. 163-172, 2023.

[11] Bagherzadeh, F., Rastgoo, R.: Deepfake image detection using a deep hybrid convolutional neural network. Journal of Modeling in Engineering, vol. 21, no. 75, pp. 19-28, 2023.

[12] Ahmadi, AM., Kiani, K., Rastgoo, R.: A Transformer-based model for abnormal activity recognition in video. Journal of Modeling in Engineering, vol. 22, no. 76, pp. 213-221, 2024.

[13] Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: CVPR. (2014)

[14] Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. In: ICLR. (2014)

[15] Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. (2015)

[16] Y. Wang et al., "DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12, 2022, Art no. 4412612, doi: 10.1109/TGRS.2022.3197546.

[17] Peng, D.; Zhang, Y.; Guan, H. End-to-End Change Detection for High Resolution Satellite Images Using Improved U-Net++. Remote Sens. 2019, 11, 1382. https://doi.org/10.3390/rs11111382

[18]  J. Chen et al., "DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1194-1206, 2021, doi: 10.1109/JSTARS.2020.3037893.

[19] Wang, Z., Jiang, K., Yi, P., Han, Z., & He, Z. (2020). Ultra-dense GAN for satellite imagery super-resolution. Neurocomputing, 398, 328-337.

[20] Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., & Li, D. (2020). A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment, 237, 111511.

[21] Tarasiou, M., Chavez, E., & Zafeiriou, S. (2023). ViTs for SITS: Vision Transformers for Satellite Image Time Series. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10418-10428).

[22] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing.

[23] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2022, October). Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision (pp. 205-218). Cham: Springer Nature Switzerland.

[24] Hoang, T. N., Nguyen, H. V. N., Nguyen, K. H., & Quach, L. D. (2023). Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving. International Journal of Advanced Computer Science and Applications, 14(7).

[25] Singh, N. J., & Nongmeikapam, K. (2023). Semantic segmentation of satellite images using deep-UNet. Arabian Journal for Science and Engineering, 48(2), 1193-1205.

[26] Aghalari, M., Aghagolzadeh, A., & Ezoji, M. (2021). Brain tumor image segmentation via asymmetric/symmetric UNet based on two-pathway-residual blocks. Biomedical signal processing and control, 69, 102841.

[27] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[28] Thakur, P. S., Sheorey, T., & Ojha, A. (2023). VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimedia Tools and Applications, 82(1), 497-520.

[29] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The Pascal visual object classes (voc) chal- lenge. International Journal of Computer Vision, 88(2):303– 338, June 2010

[30] W. R. Crum, O. Camara, and D. L. G. Hill, “Generalized overlap measures for evaluation and validation in medical image analysis.,” IEEE Trans. Med. Imaging, vol. 25, no. 11, pp. 1451– 61, Nov. 2006.

[31] Demir, Ilke, et al. "Deepglobe 2018: A challenge to parse the earth through satellite images." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2018.