Automatically and accurately conflating orthoimagery and street maps

ABSTRACT

Recent growth of the geospatial information on the web has made it possible to easily access various maps and orthoimagery. By integrating these maps and imagery, we can create intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in diverse maps. However, accurately integrating maps and imagery from different data sources remains a challenging task. This is because spatial data obtained from various data sources may have different projections and different accuracy levels. Most of the existing algorithms only deal with vector to vector spatial data integration or require human intervention to accomplish imagery to map conflation. In this paper, we describe an information integration approach that utilizes common vector datasets as "glue" to automatically conflate imagery with street maps. We present efficient techniques to automatically extract road intersections from imagery and maps as control points. We also describe a specialized point pattern matching algorithm to align the two point sets and conflation techniques to align the imagery with maps. We show that these automatic conflation techniques can automatically and accurately align maps with images of the same area. In particular, using the approach described in this paper, our system automatically aligns a set of TIGER maps for an area in El Segundo, CA to the corresponding orthoimagery with an average error of 8.35 meters per pixel. This is a significant improvement considering that simply combining the TIGER maps with the corresponding imagery based on geographic coordinates provided by the sources results in error of 27 meters per pixel.

REFERENCES

• 1: Aculair-Fortier, M.-F., Ziou, D., Armenakis, C., and Wang, S. Survey of Work on Road Extraction in Aerial and Satellite Images, Technical Report, Universite de Sherbrooke, 2000.
• 2: Agouris, P., Stefanidis, A., and Gyftakis, S. Differential Snakes for Change Detection in Road Segments, Photogrammetric Engineering & Remote Sensing, 67(12): p. 1391--1399, 2001.
• 3: Arcelli, C. and Sanniti di Baja, G. A width-independent fast thinning algorithm, IEEE Transaction on Pattern Analysis and Machine Intelligence: p. 463--474, 1985.
• 4: Mark de Berg , Marc van Kreveld , Mark Overmars , Otfried Schwarzkopf, Computational geometry: algorithms and applications, Springer-Verlag New York, Inc., Secaucus, NJ, 1997
• 5: David E. Cardoze , Leonard J. Schulman, Pattern Matching for Spatial Point Sets, Proceedings of the 39th Annual Symposium on Foundations of Computer Science, p.156, November 08-11, 1998
• 6: Chen, C.-C., Shahabi, C., and Knoblock, C.A. Utilizing Road Network Data for Automatic Identification of Road Intersections from High Resolution Color Orthoimagery, In Proceedings of the Second Workshop on Spatio-Temporal Database Management(STDBM'04), colocated with VLDB, Toronto, Canada, 2004.
• 7: Chen, C.-C., Thakkar, S., Knoblok, C.A., and Shahabi, C. Automatically Annotating and Integrating Spatial Datasets, In Proceedings of the International Symposium on Spatial and Temporal Databases, Santorini Island, Greece, 2003.
• 8: Chew, L.P., Goodrich, M.T., Huttenlocher, D.P., Kedem, K., Kleinberg, J.M., and Kravets, D. Geometric pattern matching under Euclidean motion, In Proceedings of the Fifth Canadian Conference on Computational Geometry, 1993.
• 9: Maria A. Cobb , Miyi J. Chung , Harold Foley, III , Frederick E. Petry , Kevin B. Shaw , H. Vincent Miller, A Rule-based Approach for the Conflation of Attributed Vector Data, Geoinformatica, v.2 n.1, p.7-35, March 1998 [doi>10.1023/A:1009788905049]
• 10: DARE, P. and DOWMAN, I. A new approach to automatic feature based registration of SAR and SPOT images, International Archives of Photogrammetry and Remote Sensing, 33(B2): p. 125--130, 2000.
• 11: Filin, S. and Doytsher, Y. A Linear Conflation Approach for the Integration of Photogrammetric Information and GIS Data, International archives of photogrammetry and remote sensing, 33: p. 282--288, 2000.
• 12: Flavie, M., Fortier, A., Ziou, D., Armenakis, C., and Wang, S. Automated Updating of Road Information from Aerial Images, In Proceedings of American Society Photogrammetry and Remote Sensing Conference, 2000.
• 13: Goshtasby, A. Piecewise Linear Mapping Functions for Image Registration, Pattern Recognition, 19(6): p., 1986.
• 14: Habib, A., Uebbing, R., Asmamaw, A. Automatic Extraction of Road Intersections from Raster Maps, Technical Report, The Center for Mapping, The Ohio State University., 1999.
• 15: Hild, H. and Fritsch, D. Integration of vector data and satellite imagery for geocoding, International Archives of Photogrammetry and Remote Sensing, 32(Part 4): p. 246--251, 1998.
• 16: Jung-Rae Hwang , Ji-Hyeon Oh , Ki-Joune Li, Query transformation method by delaunay triangulation for multi-source distributed spatial database systems, Proceedings of the 9th ACM international symposium on Advances in geographic information systems, November 09-10, 2001, Atlanta, Georgia, USA [doi>10.1145/512161.512171]
• 17: Sandy Irani , Prabhakar Raghavan, Combinatorial and experimental results for randomized point matching algorithms, Computational Geometry: Theory and Applications, v.12 n.1-2, p.17-31, Feb. 1999
• 18: Mohamed F. Mokbel , Walid G. Aref , Ibrahim Kamel, Performance of multi-dimensional space-filling curves, Proceedings of the 10th ACM international symposium on Advances in geographic information systems, November 08-09, 2002, McLean, Virginia, USA [doi>10.1145/585147.585179]
• 19: M. T. Musavi , M. V. Shirvaiker , E. Ramanathan , A. R. Nekovei, A vision based method to automate map processing, Pattern Recognition, v.21 n.4, p.319-326, 1988 [doi>10.1016/0031-3203(88)90045-3]
• 20: Ruprecht, D. and Muller, H. Deformed Cross-Dissolves for Image Interpolation in Scientific Visualization, The Journal of Visualization and Computer Animation: p., 1994.
• 21: Alan John Saalfeld, Conflation: automated map compilation, University of Maryland at College Park, College Park, MD, 1993
• 22: Sato, T., Sadahiro, Y., and Okabe, A. A Computational Procedure for Making Seamless Map Sheets, Center for Spatial Information Sciences, University of Tokyo, 2001.
• 23: Sebok, T.J., Roemer, L.E., and Malindzak, J., G.S. An Algorithm for Line Intersection Identification, Pattern Recognition, 13(2): p. 159--166, 1981.
• 24: Usery, E.L., Finn, M.P., and Starbuck, M. Data Integration of Layers and Features for The National Map, In Proceedings of American Congress on Surveying and Mapping, Phoenix, AZ, 2003.
• 25: White, M.S. and Griffin, P. Piecewise Linear Rubber-Sheet Map Transformation, The American Cartographer, 12(2): p.123--131,1985.
• 26: Yuan, S. and Tao, C. Development of Conflation Components., In Proceedings of Geoinformatics, Ann Arbor, Michigan, USA, 1999.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Spatial databases and GIS

General Terms:
Algorithms, Design

Keywords:
conflation, orthoimagery, point pattern matching, street maps