, Volume 12, Issue 3, pp 377–410 | Cite as

Automatically and Accurately Conflating Raster Maps with Orthoimagery

  • Ching-Chien ChenEmail author
  • Craig A. Knoblock
  • Cyrus Shahabi


Recent growth of geospatial information online has made it possible to access various maps and orthoimagery. Conflating these maps and imagery can create images that combine the visual appeal of imagery with the attribution information from maps. The existing systems require human intervention to conflate maps with imagery. We present a novel approach that utilizes vector datasets as “glue” to automatically conflate street maps with imagery. First, our approach extracts road intersections from imagery and maps as control points. Then, it aligns the two point sets by computing the matched point pattern. Finally, it aligns maps with imagery based on the matched pattern. The experiments show that our approach can conflate various maps with imagery, such that in our experiments on TIGER-maps covering part of St. Louis county, MO, 85.2% of the conflated map roads are within 10.8 m from the actual roads compared to 51.7% for the original and georeferenced TIGER-map roads.


conflation orthoimagery street raster maps vector data point pattern matching rubber sheeting 



This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0238560 (PECASE), and IIS-0324955 (ITR), and in part by the Air Force Office of Scientific Research under grant numbers FA9550-04-1-0105, FA9550-07-1-0416 and FA9550-06-C-0120, and in part by the Department of Homeland Security under ONR grant number N00014-07-1-0149.

The U.S. Government is authorized to reproduce and distribute reports for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of any of the above organizations or any person connected with them.


  1. 1.
    M.-F. Aculair-Fortier, D. Ziou, C. Armenakis and S. Wang. “Survey of work on road extraction in aerial and satellite images”. Technical Report. Universite de Sherbrooke, (2000).Google Scholar
  2. 2.
    P. Agouris, A. Stefanidis and S. Gyftakis. “Differential Snakes for Change Detection in Road Segments”. Photogrammetric Engineering & Remote Sensing, Vol. 67(12):1391–1399, December, 2001.Google Scholar
  3. 3.
    T. Barclay, J. Gray and D. Stuz. “Microsoft TerraServer: a spatial data warehouse”. In the Proceedings of ACM SIGMOD 2000, 307–318 2000.Google Scholar
  4. 4.
    M.D. Berg, M.V. Kreveld, M. Overmars and O. Schwarzkopf. “Computational geometry: algorithms and applications”, Springer-Verlag 1997.Google Scholar
  5. 5.
    R. Cao and C.L. Tan. “Text/graphics separation in maps”. In the Proceedings of the 4th International Workshop on Graphics Recognition Algorithms and Applications, Ontario, Canada, pp. 167–177, 2001 September 7–8.Google Scholar
  6. 6.
    D.E. Cardoze and L.J. Schulman. “Pattern matching for spatial point sets”. In the Proceedings of IEEE Symposium on Foundations of Computer Science, 156–165 1998.Google Scholar
  7. 7.
    C.-C. Chen. “Automatically and accurately conflating road vector data, street maps and orthoimagery”. Ph.D. Dissertation. Computer Science Department. University of Southern California. Los Angeles, CA, 2005.Google Scholar
  8. 8.
    C.-C. Chen, C.A. Knoblock and C. Shahabi. “Automatically conflating road vector data with orthoimagery”. Geoinformatica, Vol. 10(4):495–530, 2006 December.CrossRefGoogle Scholar
  9. 9.
    C.-C. Chen, C.A. Knoblock, C. Shahabi, Y.-Y. Chiang and S. Thakkar. “Automatically and accurately conflating orthoimagery and street maps”. In the Proceedings of the 12th ACM International Symposium on Advances in Geographic Information Systems (ACM-GIS’04), ACM Press, Washington, D.C, pp. 47–56, 2004 November 12–13.Google Scholar
  10. 10.
    C.-C. Chen, C. Shahabi, C.A. Knoblock and M. Kolahdouzan (2006a). “Automatically and efficiently matching road networks with spatial attributes in unknown geometry systems”. In the Proceedings of the Third Workshop on Spatio-Temporal Database Management (co-located with VLDB2006), Seoul, Korea, pp. 1–8, September 2006.Google Scholar
  11. 11.
    C.-C. Chen, S. Thakkar, C.A. Knoblok and C. Shahabi. “Automatically annotating and integrating spatial datasets”. In the Proceedings of the International Symposium on Spatial and Temporal Databases, LNCS 2750,Springer-Verlag, Santorini Island, Greece, pp. 469–488, July 24–27, 2003.Google Scholar
  12. 12.
    L.P. Chew, M.T. Goodrich, D.P. Huttenlocher, K. Kedem, J.M. Kleinberg and D. Kravets. “Geometric pattern matching under Euclidean motion”. In the Proceedings of the Fifth Canadian Conference on Computational Geometry, pp. 151–156, 1993.Google Scholar
  13. 13.
    Y.-Y. Chiang, C.A. Knoblock and C.-C. Chen. “Automatic extraction of road intersections from raster maps”. In the Proceedings of the 13th ACM International Symposium on Advances in Geographic Information Systems, Bremen, Germany, pp. 267–276, November 4–5th, 2005.Google Scholar
  14. 14.
    M. Cobb, M.J. Chung, V. Miller, H.I. Foley, F.E. Petry and K.B. Shaw. “A rule-based approach for the conflation of attributed vector data”. GeoInformatica, Vol. 2(1):7–35, 1998.CrossRefGoogle Scholar
  15. 15.
    P. Dare and I. Dowman. “A new approach to automatic feature based registration of SAR and SPOT images”. International Archives of Photogrammetry and Remote Sensing, Vol. 33(B2):125–130, 2000.Google Scholar
  16. 16.
    S. Filin and Y. Doytsher. “A linear conflation approach for the integration of photogrammetric information and GIS data”. International Archives of Photogrammetry and Remote Sensing, Vol. 33:282–288, 2000.Google Scholar
  17. 17.
    M.A. Fischler and R.C. Bolles. “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”. Communications of the ACM, Vol. 24(6):381–395, 1981.CrossRefGoogle Scholar
  18. 18.
    M. Flavie, A. Fortier, D. Ziou, C. Armenakis and S. Wang. “Automated updating of road information from aerial images”. In the Proceedings of American Society Photogrammetry and Remote Sensing Conference, Amsterdam, Holland, July, 16–23, 2000.Google Scholar
  19. 19.
    M.F. Goodchild and G.J. Hunter. “A simple positional accuracy measure for linear features”. International Journal of Geographic Information Sciences, Vol. 11(3):299–306, 1997.CrossRefGoogle Scholar
  20. 20.
    A. Habib, Uebbing, R., Asmamaw, A. “Automatic extraction of primitives for conflation of raster maps”. Technical Report. The Center for Mapping, The Ohio State University, 1999.Google Scholar
  21. 21.
    H. Hild and D. Fritsch. “Integration of vector data and satellite imagery for geocoding”. International Archives of Photogrammetry and Remote Sensing, Vol. 32(Part 4):246–251, 1998.Google Scholar
  22. 22.
    J.-R. Hwang, J.-H. Oh and K.-J. Li. “Query transformation method by Delaunay triangulation for multi-source distributed spatial database systems”. In the Proceedings of the 9th ACM Symposium on Advances in Geographic Information Systems, pp. 41–46, November 9–10, 2001.Google Scholar
  23. 23.
    S. Irani and P. Raghavan. “Combinatorial and experimental results for randomized point matching algorithms”. Computational Geometry, Vol. 12(1–2):17–31, 1999.CrossRefGoogle Scholar
  24. 24.
    M.T. Musavi, M.V. Shirvaikar, E. Ramanathan and A.R. Nekovei. “A vision based method to automate map processing”. Pattern Recognition, Vol. 21(4):319–326, 1988.CrossRefGoogle Scholar
  25. 25.
    A. Saalfeld. “Conflation: automated map compilation”. International Journal of Geographic Information Sciences, Vol. 2(3):217–228, 1988.CrossRefGoogle Scholar
  26. 26.
    A. Saalfeld. “Conflation: automated map compilation”. Computer Vision Laboratory, Center for Automation Research, University of Maryland, 1993.Google Scholar
  27. 27.
    T. Sato, Y. Sadahiro and A. Okabe. “A computational procedure for making seamless map sheets”. Technical Report. Center for Spatial Information Sciences, University of Tokyo, 2001.Google Scholar
  28. 28.
    T.J. Sebok, L.E. Roemer and J. Malindzak, G.S. “An algorithm for line intersection identification”. Pattern Recognition, Vol. 13(2):159–166, 1981.CrossRefGoogle Scholar
  29. 29.
    G. Seedahmed and L. Martucci. “Automated image registration using geometrically invariant parameter space clustering (GIPSC)”. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34(3A):318–323, 2002.Google Scholar
  30. 30.
    V. Walter and D. Fritsch. “Matching spatial data sets: a statistical approach”. International Journal of Geographic Information Sciences, Vol. 5(1):445–473, 1999.CrossRefGoogle Scholar
  31. 31.
    M.S. White and P. Griffin. “Piecewise linear rubber-sheet map transformation”. The American Cartographer, Vol. 12(2):123–131, 1985.CrossRefGoogle Scholar
  32. 32.
    S. Yuan and C. Tao. “Development of conflation components.”In the Proceedings of Geoinformatics, pp. 19–21, 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ching-Chien Chen
    • 1
    Email author
  • Craig A. Knoblock
    • 2
  • Cyrus Shahabi
    • 3
  1. 1.Geosemble TechnologiesEl SegundoUSA
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  3. 3.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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