Exploring The Application of Deep Learning to Road Detection in Remote Sensing Images

Kun Wang

doi:10.54097/5texn977

Authors

Kun Wang

DOI:

https://doi.org/10.54097/5texn977

Keywords:

deep learning, remote sensing images, road detection techniques.

Abstract

Road information is a fundamental dataset in urban planning, traffic management, and related fields. However, challenges such as complex backgrounds, occlusions, and texture similarities with other linear features reduce the accuracy of road detection using remote sensing technology. This paper explores the advantages and recent developments in deep learning for enhancing road detection from remotely sensed images. The findings indicate that encoder-decoder semantic segmentation networks enable accurate road extraction; multi-scale feature fusion techniques enhance performance in occluded scenes; the integration of multi-task topology-constrained post-processing ensures road network connectivity and suppresses noise; and the adoption of weak supervision and lightweight models reduces labeling costs and improves deployment efficiency. Collectively, these advances will help remote sensing-based road detection reach new heights in terms of accuracy, intelligence, and breadth of application, driving the development of digital cities and intelligent transportation. This paper summarizes relevant techniques that support intelligent interpretation of remote sensing imagery and offers a valuable reference for future research in this field.

Downloads

Download data is not yet available.

References

[1] Jiang Na. Research on Road Extraction Methods for Remote Sensing Images Based on Deep Learning [D]. Lanzhou Jiaotong University, 2023. DOI: 10.27205/d.cnki.gltec.2023.000009.

[2] Zhou, G., Song, W., Zheng, Z., Wang, S., & Yang, J. (2022). 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, 60, 5528513.

[3] Wu, Q., Tong, X., Huang, X., Jin, X., & Deng, S. (2021). Automatic Road Extraction from High-Resolution Remote Sensing Images Using a Method Based on Densely Connected Spatial Feature-Enhanced Pyramid. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3-17.

[4] Yang, X., Yao, X., Shi, Y., & Li, H. (2019). Road Detection and Centerline Extraction via Deep Recurrent Convolutional Neural Network U-Net. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 7209-7220.

[5] He, H., Yang, F., & Zhang, X. (2019). Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss. Remote Sensing, 11(9), 1015.

[6] Lin, Y., Di, K., Li, X., Cui, Y., Gao, B., & Yue, Z. (2020). Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sensing, 12(18), 3015.

[7] Ren, Y., Yu, Y., & Guan, H. (2020). DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery. Remote Sensing, 12(18), 3042.

[8] Wang, J., Song, Y., Zhang, Y., & Li, J. (2015). Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. International Journal of Remote Sensing, 36(12), 3144-3169.

[9] Henry, C., Azimi, S. M., & Merkle, N. (2018). Road Segmentation in SAR Satellite Images with Deep Fully Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 15(12), 1867-1871.

[10] Fan Jinhong. Research and Application of Road Recognition Technology Based on Remote Sensing Images [D]. Beijing University of Posts and Telecommunications, 2021. DOI: 10.26969/d.cnki.gbydu.2021.001013.

[11] Xu Miao, Li Yuanxhang, Zhong Juanjuan, et al. L-UNet: A Lightweight Cloud-Occluded Road Extraction Network [J]. Journal of Image and Graphics, 2021, 26(11): 2670-2679.

[12] Kang Liangfang. Research on Road Extraction from High-Resolution Remote Sensing Images Based on a Lightweight Convolutional Attention Network [D]. Guangxi Normal University, 2021. DOI: 10.27036/d.cnki.ggxsu.2021.000552.

[13] Wei, Y., & Ji, S. (2022). Scribble-Based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 60, 5520413.

[14] Huang, X. Research on road detection based on deep learning [D]. Guangxi University, 2020. DOI: 10.27034/d.cnki.ggxiu.2020.000781.

[15] Lu, X., Feng, J., Song, Y., & Tao, Y. (2019). Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9362-9377.

[16] Peng Bo. Research on Deep Learning-Based Remote Sensing Image Road Information Extraction Algorithm [D]. University of Electronic Science and Technology of China, 2019.

[17] Liu, Y., Peng, J., Zhang, Y., & Liu, H. (2019). Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional Encoder-Decoder with Spatial Pyramid Pooling. IEEE Access, 7, 128774-128786.

[18] Wei, Y., Zhang, K., & Ji, S. (2020). Simultaneous Road Surface and Centerline Extraction from Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing. IEEE Transactions on Geoscience and Remote Sensing, 58(12), 8919-8931.

[19] Shao Xin. Research on vector data and remote sensing image registration based on deep learning [D]. Wuhan University, 2021. DOI: 10.27379/d.cnki.gwhdu.2021.000047.