Vol. 7 No. 1 (2026)

Standard Journal Issues

Road Network Index: A Novel Spectral Index for Urban Road Network Extraction from Landsat Imagery

Published 2026-04-19

  • Yashwant Soni
    • ,
  • Uma meena
    • ,
  • Vikash Kumar Mishra

Yashwant Soni
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Ghaziabad, Uttar Pradesh, India

Uma meena
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Ghaziabad, Uttar Pradesh, India

Vikash Kumar Mishra
School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

Abstract

Urban road information extraction from low- to medium-resolution remote sensing imagery remains challenging due to the limited spectral separability between road surfaces and surrounding urban features. This work proposes a road network index (RNI), a novel spectral index for urban road network extraction, to delineate the urban road network from Landsat satellite imagery. In the first step, the Landsat data is pre-processed to handle the atmospheric correction followed by the selection of the spectral bands used to develop RNI by performing spectral analysis. The proposed RNI is used for the identification of road information from Landsat imagery, and the road and non-road information is separated using thresholding.  Furthermore, the non-road components are eliminated by using geometric feature analysis. By applying RNI to Landsat multispectral imagery, we can extract urban road networks accurately and efficiently. Results reveal that RNI is automated, convenient in asphalt road extraction from medium-resolution satellite imagery.  Experimental results demonstrate that the proposed approach achieves an overall accuracy of 91.67%, a Kappa coefficient of 0.8334, and an F1-score of 91.64%, indicating strong agreement and balance between precision and recall. These findings confirm that RNI provides an automated and reliable solution for asphalt road extraction from medium-resolution satellite imagery, particularly in complex urban environments.

Keywords

Landsat imagery, Road extraction, Urban extraction, Remote sensing, Urban

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References

  1. J. B. Mena, ‘State of the art on automatic road extraction for GIS update: A novel classification’, Pattern Recognit Lett, vol. 24, no. 16, pp. 3037–3058, 2003, doi: 10.1016/S0167-8655(03)00164-8.
  2. P. P. Singh and R. D. Garg, ‘A two-stage framework for road extraction from high-resolution satellite images by using prominent features of impervious surfaces’, Int J Remote Sens, vol. 35, no. 24, pp. 8074–8107, 2014, doi: 10.1080/01431161.2014.978956.
  3. P. K. Soni, N. Rajpal, and R. Mehta, ‘Road Centerline Extraction From VHR Images Using SVM and Multi-Scale Maximum Response Filter’, Journal of the Indian Society of Remote Sensing, vol. 49, no. 7, pp. 1519–1532, 2021, doi: 10.1007/s12524-021-01329-2.
  4. C. Robinson et al., ‘Large scale high-resolution land cover mapping with multi-resolution data’, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 12726–12735.
  5. M. J. Khan and P. P. Singh, ‘Advanced road extraction using CNN-based U-Net model and satellite imagery’, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 5, p. 100244, 2023, doi: https://doi.org/10.1016/j.prime.2023.100244.
  6. A. Abdollahi, B. Pradhan, and N. Shukla, ‘Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network’, Journal of the Indian Society of Remote Sensing, vol. 49, no. 3, pp. 569–583, 2021, doi: 10.1007/s12524-020-01228-y.
  7. M. Drusch et al., ‘Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services’, Remote Sens Environ, vol. 120, pp. 25–36, 2012, doi: https://doi.org/10.1016/j.rse.2011.11.026.
  8. S. L. K. Reddy, C. V. Rao, P. Rajesh Kumar, R. V. G. Anjaneyulu, and B. Gopala Krishna, ‘An index based road feature extraction from LANDSAT-8 OLI images’, International Journal of Electrical and Computer Engineering, vol. 11, no. 2, pp. 1319–1336, Apr. 2021, doi: 10.11591/ijece.v11i2.pp1319-1336.
  9. Y. Jia, X. Zhang, R. Xiang, and Y. Ge, ‘Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network’, Remote Sens (Basel), vol. 15, no. 17, 2023, doi: 10.3390/rs15174193.
  10. Y. Khurana, P. K. Soni, and D. P. Bhatt, ‘SVM-based classification of multi-temporal Sentinel-2 imagery of dense urban land cover of Delhi-NCR region’, Earth Sci Inform, vol. 16, no. 2, pp. 1765–1777, 2023, doi: 10.1007/s12145-023-01008-5.
  11. Y. Hu and K. J. Zu, ‘Road extraction from remote sensing imagery based on road tracking and ribbon snake’, in KESE 2009 - 2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering, 2009, pp. 201–204. doi: 10.1109/KESE.2009.60.
  12. S. L. K. Reddy, C. V Rao, P. R. Kumar, R. V. G. Anjaneyulu, and B. G. Krishna, ‘An index based road feature extraction from LANDSAT-8 OLI images’, International Journal of Electrical and Computer Engineering, vol. 11, no. 2, p. 1319, 2021.
  13. K. Shahi, H. Z. M. Shafri, E. Taherzadeh, S. Mansor, and R. Muniandy, ‘A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery’, The Egyptian Journal of Remote Sensing and Space Science, vol. 18, no. 1, pp. 27–33, 2015, doi: https://doi.org/10.1016/j.ejrs.2014.12.003.
  14. X. G. Y. Y. X. X. Kun Jia Xiangqin Wei and B. Li, ‘Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China’, Geocarto Int, vol. 29, no. 8, pp. 941–951, 2014, doi: 10.1080/10106049.2014.894586.
  15. P. K. Soni, N. Rajpal, R. Mehta, and V. K. Mishra, ‘Urban land cover and land use classification using multispectral sentinal-2 imagery’, Multimed Tools Appl, 2021, doi: 10.1007/s11042-021-10991-0.
  16. V. F. Rodriguez-Galiano, M. Chica-Olmo, F. Abarca-Hernandez, P. M. Atkinson, and C. Jeganathan, ‘Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture’, Remote Sens Environ, vol. 121, pp. 93–107, 2012, doi: https://doi.org/10.1016/j.rse.2011.12.003.
  17. M. Singh and K. D. Tyagi, ‘Pixel based classification for Landsat 8 OLI multispectral satellite images using deep learning neural network’, Remote Sens Appl, vol. 24, p. 100645, 2021, doi: https://doi.org/10.1016/j.rsase.2021.100645.
  18. B. Slagter et al., ‘Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning’, Remote Sens Environ, p. 114380, 2024, doi: https://doi.org/10.1016/j.rse.2024.114380.
  19. Z. Xu, K. Guan, N. Casler, B. Peng, and S. Wang, ‘A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 144, pp. 423–434, 2018, doi: https://doi.org/10.1016/j.isprsjprs.2018.08.005.
  20. R. Lian, W. Wang, N. Mustafa, and L. Huang, ‘Road extraction methods in high-resolution remote sensing images: A comprehensive review’, IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 13, pp. 5489–5507, 2020, doi: 10.1109/JSTARS.2020.3023549.
  21. A. Gruen and H. Li, ‘Semi-automatic linear feature extraction by dynamic programming and LSB-snakes’, Photogramm Eng Remote Sensing, vol. 63, no. 8, pp. 985–994, 1997.
  22. A. P. Dal Poz, R. B. Zanin, and G. M. do Vale, ‘Automated extraction of road network from medium-and high-resolution images’, Pattern Recognition and Image Analysis, vol. 16, no. 2, pp. 239–248, 2006, doi: 10.1134/S1054661806020118.
  23. D. Herumurti, K. Uchimura, G. Koutaki, and T. Uemura, ‘Urban road extraction based on hough transform and region growing’, in Frontiers of Computer Vision,(FCV), 2013 19th Korea-Japan Joint Workshop on, 2013, pp. 220–224.
  24. H. R. R. Bakhtiari, A. Abdollahi, and H. Rezaeian, ‘Semi automatic road extraction from digital images’, Egyptian Journal of Remote Sensing and Space Science, vol. 20, no. 1, pp. 117–123, 2017, doi: 10.1016/j.ejrs.2017.03.001.
  25. L. Risser, F. Plouraboue, and X. Descombes, ‘Gap Filling of 3-D Microvascular Networks by Tensor Voting’, IEEE Trans Med Imaging, vol. 27, no. 5, pp. 674–687, 2008, doi: 10.1109/TMI.2007.913248.
  26. P. N. Anil and S. Natarajan, ‘Road Extraction Using Topological Derivative and Mathematical Morphology’, Journal of the Indian Society of Remote Sensing, vol. 41, no. 3, pp. 719–724, 2013, doi: 10.1007/s12524-012-0231-6.
  27. R. Liu, J. Song, Q. Miao, P. Xu, and Q. Xue, ‘Road centerlines extraction from high resolution images based on an improved directional segmentation and road probability’, Neurocomputing, vol. 212, pp. 88–95, 2016, doi: 10.1016/j.neucom.2016.03.095.
  28. W. Shi, Z. Miao, and J. Debayle, ‘An integrated method for urban main-road centerline extraction from optical remotely sensed imagery’, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 6, pp. 3359–3372, 2014, doi: 10.1109/TGRS.2013.2272593.
  29. F. E. L. J. G. Masek and S. N. Goward, ‘Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations’, Int J Remote Sens, vol. 21, no. 18, pp. 3473–3486, 2000, doi: 10.1080/014311600750037507.
  30. J. G. Y. Zha and S. Ni, ‘Use of normalized difference built-up index in automatically mapping urban areas from TM imagery’, Int J Remote Sens, vol. 24, no. 3, pp. 583–594, 2003, doi: 10.1080/01431160304987.
  31. P. Mhangara, J. Odindi, L. Kleyn, and H. Remas, ‘Road extraction using object oriented classification’, Visualisation technical, pp. 45–50, 2011.
  32. M. W. Ahmed, S. Saadi, and M. Ahmed, ‘Automated road extraction using reinforced road indices for Sentinel-2 data’, Array, vol. 16, p. 100257, 2022, doi: https://doi.org/10.1016/j.array.2022.100257.
  33. N. Otsu, ‘A threshold selection method from gray-level histograms’, IEEE Trans Syst Man Cybern, vol. 9, no. 1, pp. 62–66, 1979.
  34. P. K. Soni, N. Rajpal, and R. Mehta, ‘Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images’, Journal of the Indian Society of Remote Sensing, vol. 48, no. 3, pp. 513–524, 2020, doi: 10.1007/s12524-019-01077-4.

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