Skip to main content
SpringerLink
Log in

Using Aerial Photography for Semi-automatic Extraction of Road Network at a Scale of 1:25000

  • Chapter
  • First Online:
Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development

Part of the book series: Advances in Science, Technology & Innovation ((ASTI))

  • 508 Accesses

Abstract

Through the application of new methods of digital restitution, photogrammetry applied to the urban domain has always contributed to the improvement of the knowledge and control of urban development especially in relation to rapidly changing spaces. In this study, we seek to develop a method of extracting road network from aerial photos at a scale of 1:25000, in the area of El Marsa, a northern suburb of Tunis, Tunisia. As it is generally known, from the intensity variations on the image, the one can make the segmentation, recognize the objects and the forms, and evaluate their spatial positions as well as the relations uniting them. In this context, the roads seem to be of particular interest in urban areas. Their automatic recognition makes them useful in various tasks, including mapping, recalling multisource images, urban planning and automatic navigation. However, despite the various works carried out on this field, none of the sophisticated road detection techniques is perfect. The difficulty of road extraction is seen while establishing a statistical test to know whether a given pixel of the image participates in the road network or not. In fact, this difficulty is accentuated in the urban environment as it is characterized by a strong heterogeneity of attributes. Compared to other cartographic themes such as buildings, contour lines, and vegetation, manual road entry is fast, and the road theme appears to be the easiest one to be automatically restituted. Our major concern, throughout this study, is to elaborate a methodological approach that enables us to replace manual photogrammetric capture by automatic or a semi-automatic captures from the Orthophoto of a 30 cm resolution. In relation to the current state of art, this photogrammetric capture is advantageous in time and material. This work on automatic interpretation of aerial images thus meets the need to optimize the productivity of photogrammetric capture strings without harming the quality of the final product. The problem raised however is related to the automatic extraction of roads in 2D and its transformation into 3D with validation. Our procedure is not limited to image processing, it rather extends to:

  • Road extraction from radiometry-based segmentation using the “eCognition” software.

  • Making a mask for buildings.

While the first step with “eCognition” allows the transformation of the buildings into vector format and polygon type, the second step with “ArcScene” contributes to the juxtaposition of the roads in a continuous way on the Orthophoto. The last step of our procedure consists in the layering of the roads on a Digital Terrain Model (DTM) using the “LPS” software, with a resolution of 1 m in planimetry. The results obtained are evaluated by quantitative statistical approach. In this perspective, it is important to further develop an automatic chain of quality control and validation of the restituted road network.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Singh, P. P., & Garg, R. D. (2013). Automatic road extraction from high resolution satellite image using adaptive global thresholding and morphological operations. Journal of the Indian Society of Remote Sensing, 41(3), 631–640.

    Google Scholar 

  2. Richards, J. A. (2013). Remote sensing digital image analysis. Berlin: Springer.

    Google Scholar 

  3. Dosovitskiy, A., Fischer, P., Ilg, E., Hr̈usser, P., Hazırbas, C., Golkov, V., et al. (2015). FlowNet: Learning optical flow with convolutional networks. In IEEE International Conference on Computer Vision (ICCV).

    Google Scholar 

  4. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for scene segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.

    Google Scholar 

  5. Pinheiro, P., & Collobert, R. (2014). Recurrent convolutional neural networks for scenelabeling. In International Conference on Machine Learning (pp. 82–90).

    Google Scholar 

  6. Bajcsy, R., & Tavakoli, M. (1976). Computer recognition of roads from satellite pictures. IEEE Transactions on Systems, Man, and Cybernetics, 6(9), 623–637.

    Article  Google Scholar 

  7. Farah, N. (1998). Extraction et évaluation du réseau routier urbain à partir des images satellitaires: développement d’algorithmes. Mémoire (M.Sc) Département de géographie et télédétection Faculté des lettres et sciences humaines Université de Sherbrooke (p. 95).

    Google Scholar 

  8. Mena, J. B. (2003). State of the art on automatic road extraction for GIS update: A novel classification. Pattern Recognition Letters, 24(16), 3037–3058.

    Article  Google Scholar 

  9. Ruskoné, R., & Airault, S. (1997). Toward an automatic extraction of the road network by local interpretation of the scene. In 46th Photogrammetric Week, Stuttgart.

    Google Scholar 

  10. Yoon, H. S., Hackett, J., & Bhattacharya, D. (2002). Proceedings of the National Academy of Sciences of the USA, 99, 11724–11729.

    Article  Google Scholar 

  11. Faber, A., & Förstner, W. (1999). Scale characteristics of local autocovariances for texture segmentation. International Archives of Photogrammetry and Remote Sensing, 32(7–4–3), Valladolid, Spain.

    Google Scholar 

  12. Fischler, M., Tenenbaum, J. M., & Wolf, H. C. (1981). Detection of roads and linear structures in low-resolution herid hagery using an mdtisource knowledge integration technique. Computer Graphics Image Process, 15, 201–223.

    Article  Google Scholar 

  13. Amini, A. A., Weymouth, T. E., & Jain, R. C. (1990). Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis & Machine Intelligence, 12, 855–867.

    Article  Google Scholar 

  14. Stoica, R., Descombes, X., & Zérubia, J. (2000). A Markov point process for road extraction in remote sensed images. Technical Report 3923, Rapport de recherche de l’INRIA.

    Google Scholar 

  15. Dal Poz, A. P., Gyftakis, S., & Agouris, P. (2000). Semiautomatic road extraction: Comparison of methodologies and experiments. In 2000 ASPRS Annual Conference, Washington, DC, USA.

    Google Scholar 

  16. Dal Poz, A. P. & Vale, G. M. (2003). Dynamic programming approach for semi-automated road extraction from medium- and high-resolution images. ISPRS Archives, 34(3/W8), 87–91, Munich.

    Google Scholar 

  17. Lassalle, P. (2015). Étude du passage à l’échelle des algorithmes de segmentation et de classification en télédétection pour le traitement de volumes massifs de données, Doctorat de l’université de Toulouse.

    Google Scholar 

  18. Kass, M., & Witkin, A. (1988). Snakes: Active contours models. International Journal of Computer Vision, 1(4), 321–331.

    Article  Google Scholar 

  19. Steger, C., Mayer, H., Eckstein, W., Heinrich, E., & Baumgartner, A. (1999). Automatic road extraction based on multi-scale, grouping, and context. Photogrammetric Engineering and Remote Sensing, 65(7), 777–785.

    Google Scholar 

  20. Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., & Baumgartner, A. (2000). Automatic extraction of roads from aerial images based on scale space and snakes. Machine Vision and Applications, 12, 23–31.

    Google Scholar 

  21. Krähenbühl, P., & Koltun, V. (2011). Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in neural information processing systems.

    Google Scholar 

  22. Chen, L. C., Barron, J. T., Papandreou, G., Murphy, K., & Yuille, A. L. (2016). Semantic image segmentation with task-specific edge detection using

    Google Scholar 

  23. Pinheiro, P. O., Lin, T. Y., Collobert, R., & Dollár, P. (2016). Learning to refine object segments. European Conference on Computer Vision (pp. 75–91). Berlin: Springer.

    Google Scholar 

  24. Wang, J., & Howarth, P. J. (1987). Autornated road network extraction from Landsat TM imagery. ASPRS-ACSM, Annual Convention, 1, 429–438.

    Google Scholar 

  25. Ton, J., Jain, A. K., Enslin, W. R., & Hudson, W. D. (1989). Automatic road identification and labelling in Landsat 4 TM images. Photogrammetria, 43, 276–357.

    Article  Google Scholar 

  26. Wang, J., Treitz, P. M., & Howarth, P. J. (1992). Road network detection from SPOT imagery for updating geogaphical information systems in the rural- urban fringe. International Journal of Geographical Information Systems, 6(2), 141–157.

    Article  Google Scholar 

  27. Wang, T.-Q, Liu, X.-W., & Yin, H.-S, (1994). A new species of Hymenopus (Mantodea Hymenopodidae: Hymenopodinae) from China. Entomotaxonomia, 16(2), 79–81.

    Google Scholar 

  28. Matheron, G. (1975). Random sets and integral geometry bull. American Mathematical Society, 81, 844–847.

    Google Scholar 

  29. Serra, J. (1983). Image analysis and mathematical morphology. \(\square \)Cytometry (pp. 184–185). https://doi.org/10.1002/cyto.990040213.

    Article  Google Scholar 

  30. O’Brien, D. (1991). Computer assisted feature extraction (InterEx). In Proceeding of I4th. Canadian Symposium on Rernote Sensing (pp. 423–427).

    Google Scholar 

  31. Wang, D., He, D.-C, Wang, L., & et Morin, D. (1996). L’extraction du réseau routier urbain à partir d’images SPOT HRV. International Journal of Remote Sensing, 17(4), 827–833.

    Google Scholar 

  32. Wang, J., Song, J., Chen, M., & Yang, Z. (2015). Road network extraction: A neural dynamic framework based on deep learning and a finite state machine. International Journal of Remote Sensing, 36, 3144–3169.

    Article  Google Scholar 

  33. Khesali, E., Zoej, M. J. V., Mokhtarzade, M., & Dehghani, M. (2016). Semi automatic road extraction by fusion of high resolution optical and radar image. Journal of the Indian Society of Remote Sensing, 44(1), 21–29.

    Article  Google Scholar 

  34. Reza, H., Riahi, B., Abolfazl, A., Rezaeian, H. (2017). Semi automatic road extraction from digital images. The Egyptian Journal of Remote Sensing and Space Sciences, 20(2017), 117–123.

    Article  Google Scholar 

  35. Zerubia, J., & et Merlet, N. (1993). Classical Mechanics and Roads Detection in SPOT Images. Rapport de Recherche no 1889, lnstitut National de Recherche en Informatique et en Automatique. France. 52 p.

    Google Scholar 

  36. Cleynenbreugel, J. V., Fierens, F., Suetens, P., & Oosterlink, A. (1990). Delineating road structure on satellite imagery by a GIS-guided technique. Photogrammetry and Remote Sensing, 56(6), 893–898.

    Google Scholar 

  37. Jedynak, B. (1995). Modèles stochastiques et méthodes déterministes pour extraire les routes des images de la terre vues du ciel”. Thèse de Doctorat: Université Paris-Sud, France (p. 186).

    Google Scholar 

  38. Vosselman, G., & De Knecht, J. (1995). Road tracking by profile matching and Kalman filtering. In Gruen A., Kuebler O., Agouris P. (Eds.), Workshop on Automatic Extraction of Man-Made Objects from Aerial and Space Images. Birkhauser, Basel (pp. 265–274).

    Google Scholar 

  39. Tamokoue, H. O. (2011). Etude exploratoire de méthodes d’extraction automatique du réseau routier à partir d’images satellites haute résolution. ENSG, 2011, 15–16.

    Google Scholar 

  40. Sehad, M. (2014). Segmentation d’images par une approche basée sur des caractéristiques texturales, temporelles et spectrales: Application aux images MSG. Thèse de doctorat en Télédétection: Université Mouloud Mammeri, Tizi Ouzou, Algérie (200 p.).

    Google Scholar 

  41. Bouziani, M., et al. (2010). Rule-based classification of very high resolution image. IEEE Transactions on Geoscience and Remote Sensing, 48(8), 3198–3211.

    Article  Google Scholar 

  42. Croquerez, J. (1995). Analyse d’images - Filtrage Et Segmentation. Edition Masson.

    Google Scholar 

  43. Deriche, R. (1987). UsingCanny’s Creteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision, 1(2), 167–187.

    Article  Google Scholar 

  44. Fortier, M. F. A., Ziou, D., Armenakis, C., & Xang, S. (1999). Nouvelles perspectives en détection de contour: textures et images multispectrales. Vision interface, trios rivière (pp. 19–21).

    Google Scholar 

  45. Xue, H., Géraud, T., & Duret-Lutz, A. (2003). Multi-band segmentation using morphological clustering and fusion- application to color image segmentation. In IEEE International Conference on Image Processing.

    Google Scholar 

  46. Martinez-Uso, A., Plat, F., & Garcia-Sevilla, P. (2005). Multispectral images segmentation by energy minimization for fruit quality estimation. In Pattern Recognition and Image Analysis (Vol. 3523, pp. 689–696).

    Google Scholar 

  47. Roussen, M., & Deriche, R. (2002). A variational framework for active and adaptive segmentation of vector valued images. In IEEE proceeding of the workshop on motion and video computing.

    Google Scholar 

  48. Roula, M. A., Bouridane, A., Kurugollo, F., & Amira, A. (2002). UN supervised segmentation of multispectral images using edge progression and cost function. In IEEE International Conference of Image Processing (Vol. 3, pp. 781–784).

    Google Scholar 

  49. Cheng, H. D., & Sun, Y. (2000). A hierarchical approach to color image segmentation using homogeneity. IEEE International Transactions on Image Processing, 9(12), 2071–2082.

    Google Scholar 

  50. Ikonomakis, N., Pataniotis, K. N., & Venetsanopoulos, A. N. (2000). Unsupervised seed determination for a region-based color image segmentation Scheme. In Proceedings of the international conference on image processing, 10–13 September, Vancouver, BC, Vol. 1, pp. 537–540.

    Google Scholar 

  51. Voisine, N. (2002). Approche adaptative de coopération hiérarchique de méthodes de segmentation, application aux images multicomposantes. Ph.D thesis, Université de Rennes 1, 250p.

    Google Scholar 

  52. Tremeau, A., Colantoni, P. (2000). Regions adjacency’s graph applied to color image segmentation. IEEE Transactions on Image Processing, 9(4), 735–744.

    Article  Google Scholar 

  53. Li, P., & Xiao, X. (2007). Multispectral image segmentation by a multichannel Watershed-bases approach. International Journal of Remote Sensing, 28(19), 4429–4452.

    Google Scholar 

  54. Volpi, M., & Tuia, D. (2017). ense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55, 881–893.

    Google Scholar 

  55. Zhang, R., Sun, D., Yu, Y., & Goldberg, M. D. (2012). Mapping night time flood from MODIS observations using support vector machines. Photogrammetric Engineering and Remote Sensing, 78, 1151–1161.

    Google Scholar 

  56. Girard, M. C., & Girard, C. M. (2003). Processing of remote sensing data (487p). The Netherlands: Balkema.

    Google Scholar 

  57. Bou Kheir, R., Greve, M. H., Deroin, J. P., & Rebai, N. (2011). Implementing GIS regression trees for generating the spatial distribution of copper in Mediterranean environments: The case study of Lebanon. International Journal of Environmental Analytical Chemistry, 20. https://doi.org/10.1080/03067319.2011.603079.

    Article  Google Scholar 

  58. Rebai, N., Slama, T., & Turki, M. M. (2007). Evaluation de différentes méthodes d’interpolation spatiale pour laproduction d’un MNT á partir des données topographiques dans un SIG. Revue XYZ, 110(1er trimester).

    Google Scholar 

  59. Kneale, P., See, L., & Smith, A. (2001). Towards defining measures for neural network forecasting models. In Proceeding of the 6th International Conference on Geocomputation, University of Queensland, 24–26 September 2001, Brisbane, Australia, pp. 61–72.

    Google Scholar 

  60. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models, I, A discussion of principals. Journal of Hydrology, 10(1970), 282–290.

    Google Scholar 

  61. Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(1981), 148–194.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ENSI: Ecole Nationale des sciences de l’Informatique Manouba, Manouba, Tunisia

    Karim Mansouri

  2. LR14ES03 Geotechnical Engineering and Georisk Laboratory, National School of Engineering of Tunis, University of Tunis El Manar, le Belvédère 1002, B.P. 37, Tunis, Tunisia

    Noamen Rebai

  3. CNCT: Centre National de la Cartographie et de la Télédétection (en Tunisie), Tunis, Tunisie

    Sahar Gaaloul & Murad Salhi

Authors
  1. Karim Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noamen Rebai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sahar Gaaloul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Murad Salhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karim Mansouri .

Editor information

Editors and Affiliations

  1. National Engineering School of Tunis, Tunis, Tunisia

    Noamen Rebai

  2. Geopac Research Center, Scientific Institute, Mohammed V University of Rabat, Rabat, Morocco

    Mohamed Mastere

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mansouri, K., Rebai, N., Gaaloul, S., Salhi, M. (2020). Using Aerial Photography for Semi-automatic Extraction of Road Network at a Scale of 1:25000. In: Rebai, N., Mastere, M. (eds) Mapping and Spatial Analysis of Socio-economic and Environmental Indicators for Sustainable Development. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-21166-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation