Road Boundary Extraction Using Shadow Path Reconstruction in Urban Areas

  • Kong-Hyun Yun
  • Hong-Gyoo Sohn
  • Joon Heo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3981)


High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a road boundary extraction technique that combines information from aerial color image and LIght Detection And Ranging (LIDAR) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LIDAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadowpath reconstruction algorithms. After that, road boundary extraction is implemented by segmentation, edge detection, and edge linking method. Finally, road boundary lines are extracted as vector data by vectorization technique. The experimental results show that this approach is effective and great potential advantages.


Lidar Data Shadow Region Digital Surface Model Solar Elevation Asphalt Road 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wolf, P.R., Dewitt, B.A.: Elements of Photogrammetry: With Applications in GIS, 3rd edn., p. 608. McGraw-Hill, Boston (2000)Google Scholar
  2. 2.
    Gruen, A., Li, H.H.: Semi-automatic liner feature extraction by dynamic programming and LSB-snakes. Photogrammetric Engineering & Remote Sensing 63(8), 985–995 (1997)Google Scholar
  3. 3.
    Itten, K.I., Meyer, P.: Geometric and radiometric correction of TM data of mountainous forested areas. IEEE Transactions on Geoscience and Remote Sensing 31(4), 764–770 (1993)CrossRefGoogle Scholar
  4. 4.
    Trinder, J., Li, H.H.: Semi-Automatic Feature Extraction by Snakes. In: Gruen, A., Kubler, O., Agouris, P. (eds.) Automatic Extraction of Man-Made Objects from Aerial and Space Images, pp. 95–104. Birkhauser Verlag, Basel (1995)Google Scholar
  5. 5.
    Neuenschwander, W., Fua, P., Szekely, G., Kubler, O.: From zip lock snakes to VelcroTM surfaces. In: Gruen, A., Kubler, O., Agouris, P. (eds.) Automatic Extraction of Man-Made Objects from Aerial and Space Images, pp. 105–114. Birkhauser Verlag, Basel (1995)Google Scholar
  6. 6.
    Zhu, P., Lu, Z., Chen, X., Honda, K., Eiumnoh, A.: Extraction of city roads through path reconstruction using laser data. Photogrammetric Engineering and Remote Sensing 70(12), 1433–1440 (2004)Google Scholar
  7. 7.
    Stockham, J.T.G.: Image processing in the context of a visual model. Proceeding of the IEEE 60, 828–842 (1972)CrossRefGoogle Scholar
  8. 8.
    Cowen, D.J., Jensen, J.R., Hendrix, C., Hodgson, M.E., Schill, S.R.: A GIS-Assisted rail construction econometric model that incorporates LiDAR data. Photogrammetric Engineering and Remote Sensing 66(11), 1323–1328 (2000)Google Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital image processing. Addison-Wesley, New York (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kong-Hyun Yun
    • 1
  • Hong-Gyoo Sohn
    • 2
  • Joon Heo
    • 2
  1. 1.Civil Engineering Research InstituteYonsei UniversitySeoulKorea
  2. 2.School of Civil and Env. EngineeringYonsei UniversitySeoulKorea

Personalised recommendations