• DocumentCode
    1633589
  • Title

    A Method for Automatically Extracting Road Layers from Raster Maps

  • Author

    Chiang, Yao-Yi ; Knoblock, Craig A.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Southern California, Marina, CA, USA
  • fYear
    2009
  • Firstpage
    838
  • Lastpage
    842
  • Abstract
    To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.
  • Keywords
    data compression; geographic information systems; image coding; image colour analysis; image representation; automatic road layer pixel extraction; image colors; image compression; image scanning; raster map; road network; user input minimization; Colored noise; Computer science; Data mining; Feature extraction; Graphics; Image color analysis; Information analysis; Roads; Testing; Text analysis; Hough transformation; raster map; road; vectorization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1520-5363
  • Print_ISBN
    978-1-4244-4500-4
  • Electronic_ISBN
    1520-5363
  • Type

    conf

  • DOI
    10.1109/ICDAR.2009.274
  • Filename
    5277525