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Automatically Annotating and Integrating Spatial Datasets

  • Ching-Chien Chen
  • Snehal Thakkar
  • Craig Knoblock
  • Cyrus Shahabi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)

Abstract

Recent growth of the geo-spatial information on the web has made it possible to easily access a wide variety of spatial data. By integrating these spatial datasets, one can support a rich set of queries that could not have been answered given any of these sets in isolation. However, accurately integrating geo-spatial data from different data sources is a challenging task. This is because spatial data obtained from various data sources may have different projections, different accuracy levels and different formats (e.g. raster or vector format). In this paper, we describe an information integration approach, which utilizes various geo-spatial and textual data available on the Internet to automatically annotate and conflate satellite imagery with vector datasets. We describe two techniques to automatically generate control point pairs from the satellite imagery and vector data to perform the conflation. The first technique generates the control point pairs by integrating information from different online sources. The second technique exploits the information from the vector data to perform localized image-processing on the satellite imagery. Using these techniques, we can automatically integrate vector data with satellite imagery or align multiple satellite images of the same area. Our automatic conflation techniques can automatically identify the roads in satellite imagery with an average error of 8.61 meters compared to the original error of 26.19 meters for the city of El Segundo and 7.48 meters compared to 15.27 meters for the city of Adams Morgan in Washington, DC.

Keywords

Control Point Intersection Point Road Network Vector Data Road Segment 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ching-Chien Chen
    • 1
  • Snehal Thakkar
    • 1
  • Craig Knoblock
    • 1
  • Cyrus Shahabi
    • 1
  1. 1.Department of Computer Science & Information Sciences InstituteUniversity of Southern CaliforniaLos Angeles

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