Skip to main content
Log in

A critical analysis of road network extraction using remote sensing images with deep learning

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

The Extraction of Roads from Remote Sensing Imagery is a rapidly developing field that has significant impacts on both the economic and social domains. In the fields of urban planning, transportation management, and disaster response, accurate and up-to-date road information obtained from satellite and aerial images is essential. Through an in depth-analysis of the existing research, this study identified the research gaps and proposed a framework for Road Extraction. The data for review is collected from the IEEE Xplore, Scopus and Web of Science where 2018–2023 publications are considered. To review the facts, 1198 articles are extracted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. After meeting the exclusion and inclusion criteria, 44 articles are identified for final considerations. In this study, a thorough investigation on road network model and road features in the context of Remote Sensing Images is discussed. Additionally, we identified a clear gap in the literature where these important elements have either not been thoroughly investigated or not mentioned at all. This paper contributes to the field of Road Extraction by providing accessible datasets with links for researchers. A comparative analysis of existing Deep Learning models is conducted, aiding researchers in model selection. Furthermore, limitations and challenges faced by researchers are highlighted, offering valuable insights for future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

Authors declare that all the data being used in the design and production cum layout of the manuscript is declared in the manuscript.

References

  1. Zhang, B., Wu, Y., Zhao, B., Chanussot, J., Hong, D., Yao, J., & Gao, L. (2022). Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1814–1822.

    ADS  Google Scholar 

  2. Lian, R., Wang, W., Mustafa, N., & Huang, L. (2020). Road extraction methods in high-resolution remote sensing images: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5489–5507.

    ADS  Google Scholar 

  3. Zang, N., Cao, Y., Wang, Y., Huang, B., Zhang, L., & Mathiopoulos, P. T. (2021). Land-use mapping for high-spatial resolution remote sensing image via deep learning: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5372–5391.

    ADS  Google Scholar 

  4. Lu, X., Zhong, Y., Zheng, Z., Chen, D., Su, Y., Ma, A., & Zhang, L. (2022). Cascaded multi-task road extraction network for road surface, centerline, and edge extraction. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.

    Google Scholar 

  5. Liu, R., Miao, Q., Zhang, Y., Gong, M., & Xu, P. (2019). A semi-supervised high-level feature selection framework for road centerline extraction. IEEE Geoscience and Remote Sensing Letters, 17(5), 894–898.

    ADS  Google Scholar 

  6. Li, X., Wang, Y., Zhang, L., Liu, S., Mei, J., & Li, Y. (2020). Topology-enhanced urban road extraction via a geographic feature-enhanced network. IEEE Transactions on Geoscience and Remote Sensing, 58(12), 8819–8830.

    ADS  Google Scholar 

  7. Vani, K. (2017). A new semi automated framework for road network extraction using remote sensing images.

  8. Xiao, Y., & Zhan, Q. (2009). A review of remote sensing applications in urban planning and management in China. 2009 Joint Urban Remote Sensing Event, pp.1–5.

  9. Wang, Y., Peng, Y., Li, W., Alexandropoulos, G. C., Yu, J., Ge, D., & Xiang, W. (2022). DDU-Net: Dual-Decoder-U-Net for road extraction using high-resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12.

    Google Scholar 

  10. Wei, Y., & Ji, S. (2021). Scribble-based weakly supervised deep learning for road surface extraction from remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12.

    Google Scholar 

  11. Dong, S., & Chen, Z. (2021). Block multi-dimensional attention for road segmentation in remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  12. Constantin, A., Ding, J. J., & Lee, Y. C. (2018). Accurate road detection from satellite images using modified u-net. In 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) (pp. 423–426). IEEE.

  13. Hemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021). A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15), p2869.

    ADS  Google Scholar 

  14. Zhu, Q., Sun, X., Zhong, Y., & Zhang, L. (2019). High-resolution remote sensing image scene understanding: A review. In IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 3061–3064). IEEE.

  15. Abdollahi, A., Pradhan, B., Sharma, G., Maulud, K. N. A., & Alamri, A. (2021). Improving road semantic segmentation using generative adversarial network. IEEE Access: Practical Innovations, Open Solutions, 9, 64381–64392.

    Google Scholar 

  16. Jiang, Y. (2019). Research on road extraction of remote sensing image based on convolutional neural network. EURASIP Journal on Image and Video Processing, 2019, 1–11.

    ADS  Google Scholar 

  17. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177.

    ADS  Google Scholar 

  18. Abdollahi, A., Pradhan, B., & Alamri, A. (2020). VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data. IEEE Access: Practical Innovations, Open Solutions, 8, 179424–179436.

    Google Scholar 

  19. Eltaher, F., Taha, A., Courtney, J., Mckeever, S., Using Satellite Images Datasets for Road Intersection Detection in Route Planning.

  20. Y., Jiang, C., Zhong, & Zhang, B. (2022). AGD-Linknet: A Road Semantic Segmentation Model for High Resolution Remote Sensing Images Integrating Attention Mechanism, Gated Decoding Block and Dilated Convolution, IEEE Access, vol. 11, no. February, pp. 22585–22595, 2023, https://doi.org/10.1109/ACCESS.2023.3253289.

  21. Yang, J., & Liu, H. (2023). Modulation learning on Fourier-Domain for Road extraction from remote sensing images. IEEE Geoscience and Remote Sensing Letters, 20, 1–5.

    CAS  Google Scholar 

  22. Dai, L., Zhang, G., & Zhang, R. (2023). RADANet: Road augmented deformable attention network for road extraction from complex high-resolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–13.

    Google Scholar 

  23. Yang, Z., Zhou, D., Yang, Y., Zhang, J., & Chen, Z. (2022). Road extraction from Satellite Imagery by Road Context and full-stage feature. IEEE Geoscience and Remote Sensing Letters, 20, 1–5.

    Google Scholar 

  24. Wu, Q., Luo, F., Wu, P., Wang, B., Yang, H., & Wu, Y. (2020). 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.

    ADS  Google Scholar 

  25. Li, J., Meng, Y., Dorjee, D., Wei, X., Zhang, Z., & Zhang, W. (2021). Automatic road extraction from remote sensing imagery using ensemble learning and postprocessing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10535–10547.

    ADS  Google Scholar 

  26. Tan, H., Xu, H., & Dai, J. (2022). BSIRNet: A road extraction network with bidirectional spatial information reasoning. Journal of Sensors, pp.1–11.

  27. Yang, K., Yi, J., Chen, A., Liu, J., & Chen, W. (2021). ConDinet++: Full-scale fusion network based on conditional dilated convolution to extract roads from remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    CAS  Google Scholar 

  28. Avcı, C., Sertel, E., & Kabadayı, M. E. (2022). Deep learning-based road extraction from historical maps. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  29. Chen, X., Sun, Q., Guo, W., Qiu, C., & Yu, A. (2022). GA-Net: A geometry prior assisted neural network for road extraction. International Journal of Applied Earth Observation and Geoinformation, 114, p103004.

    Google Scholar 

  30. Zhou, M., Sui, H., Chen, S., Liu, J., Shi, W., & Chen, X. (2022). Large-scale road extraction from high-resolution remote sensing images based on a weakly-supervised structural and orientational consistency constraint network. ISPRS Journal of Photogrammetry and Remote Sensing, 193, pp.234-251.

  31. S. Sun, Z. Yang, and T. Ma, Lightweight Remote Sensing Road Detection Network, IEEE Geosci. Remote Sens. Lett, vol. 19, pp. 2–6, 2022, https://doi.org/10.1109/LGRS.2022.3179400.

  32. Wang, Y., Seo, J., & Jeon, T. (2021). NL-LinkNet: Toward lighter but more accurate road extraction with nonlocal operations. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  33. Zao, Y., & Shi, Z. (2021). Richer U-Net: Learning more details for road detection in remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    Google Scholar 

  34. Chen, R., Li, X., Hu, Y., Wen, C., & Peng, L. (2020). Road extraction from remote sensing images in wildland–urban interface areas. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    CAS  Google Scholar 

  35. Guan, H., Yu, Y., Li, D., & Wang, H. (2021). RoadCapsFPN: Capsule feature pyramid network for road extraction from VHR optical remote sensing imagery. IEEE Transactions on Intelligent Transportation Systems, 23(8), 11041–11051.

    Google Scholar 

  36. Yang, Z., Zhou, D., Yang, Y., Zhang, J., & Chen, Z. (2022). TransRoadNet: A novel road extraction method for remote sensing images via combining high-level semantic feature and context. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.

    CAS  Google Scholar 

  37. Yuan, G., Li, J., Liu, X., & Yang, Z. (2022). Weakly supervised road network extraction for remote sensing image based scribble annotation and adversarial learning. Journal of King Saud University-Computer and Information Sciences, 34(9), 7184–7199.

    Google Scholar 

  38. Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for the road-network extraction from VHR remote sensing images–using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2662–2673.

    ADS  Google Scholar 

  39. Gu, Q., Xue, B., Ruan, S., & Li, X. (2021). A road extraction method for intelligent dispatching based on MD-LinkNeSt network in open-pit mine. International Journal of Mining Reclamation and Environment, 35(9), 656–669.

    Google Scholar 

  40. Chen, Z., Wang, C., Li, J., Zhong, B., Du, J., & Fan, W. (2021). Combined improved Dirichlet models and deep learning models for road extraction from remote sensing images. Canadian Journal of Remote Sensing, 47(3), 465–484.

    ADS  Google Scholar 

  41. Wan, J., Xie, Z., Xu, Y., Chen, S., & Qiu, Q. (2021). DA-RoadNet: A dual-attention network for road extraction from high resolution satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6302–6315.

    ADS  Google Scholar 

  42. Chen, S. B., Ji, Y. X., Tang, J., Luo, B., Wang, W. Q., & Lv, K. (2021). DBRANet: Road extraction by dual-branch encoder and regional attention decoder. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.

  43. L. Ding and L. Bruzzone, DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images, pp. 1–12.

  44. Ding, Q., Liu, H., Luo, H., & Chen, X. (2021). Road detection network based on anti-disturbance and variable-scale spatial context detector. IEEE Access: Practical Innovations, Open Solutions, 9, 114640–114648.

    Google Scholar 

  45. Li, X., Zhang, Z., Lv, S., Pan, M., Ma, Q., & Yu, H. (2021). Road extraction from high spatial resolution remote sensing image based on multi-task key point constraints. IEEE Access: Practical Innovations, Open Solutions, 9, 95896–95910.

    Google Scholar 

  46. Boonpook, W., Tan, Y., Bai, B., & Xu, B. (2021). Road extraction from uav images using a deep resdclnet architecture. Canadian Journal of Remote Sensing, 47(3), 450–464.

    ADS  Google Scholar 

  47. Gao, L., Wang, J., Wang, Q., Shi, W., Zheng, J., Gan, H., Lv, Z., & Qiao, H. (2021). Road extraction using a dual attention dilated-linknet based on satellite images and floating vehicle trajectory data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp.10428-10438.48. P. Li et al., Robust Deep Neural Networks for Road Extraction from Remote Sensing Images, IEEE Trans. Geosci. Remote Sens, vol. 59, no. 7, pp. 6182–6197, 2021, https://doi.org/10.1109/TGRS.2020.3023112.

  48. P. Li et al., Robust Deep Neural Networks for Road Extraction from Remote Sensing Images, IEEE Trans. Geosci. Remote Sens, vol. 59, no. 7, pp. 6182–6197, 2021, https://doi.org/10.1109/TGRS.2020.3023112.

  49. Yang, C., & Wang, Z. (2020). An ensemble Wasserstein generative adversarial network method for road extraction from high resolution remote sensing images in rural areas. IEEE Access: Practical Innovations, Open Solutions, 8, 174317–174324.

    Google Scholar 

  50. Zhou, M., Sui, H., Chen, S., Wang, J., & Chen, X. (2020). BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 288–306.

    ADS  Google Scholar 

Download references

Funding

The authors received no specific funding for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anand Nayyar.

Ethics declarations

Ethical approval

No Human subject or animals are involved in the research.

Consent to participate

All authors have mutually consented to participate.

Consent to publish

All the authors have consented the Journal to publish this paper.

Conflict of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, P., Kumar, R., Gupta, M. et al. A critical analysis of road network extraction using remote sensing images with deep learning. Spat. Inf. Res. (2024). https://doi.org/10.1007/s41324-024-00576-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41324-024-00576-y

Keywords

Navigation