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Automatically Annotating and Integrating Spatial Datasets

Automatically Annotating and Integrating Spatial Datasets. Chieng-Chien Chen, Snehal Thakkar, Crail Knoblock, Cyrus Shahabi Department of Computer Science &Information Technologies University of Southern California Discussant: Oncel Tuzel. Outline. Problem Definition Finding Control Points

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Automatically Annotating and Integrating Spatial Datasets

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  1. Automatically Annotating and Integrating Spatial Datasets Chieng-Chien Chen, Snehal Thakkar, Crail Knoblock, Cyrus Shahabi Department of Computer Science &Information Technologies University of Southern California Discussant: Oncel Tuzel

  2. Outline • Problem Definition • Finding Control Points • Filtering Control Points • Integration of Data Sources • Performance Evaluation • Conclusion

  3. Problem Definition • Automatic integration of data sources having: • Different projections • Different accuracy • Different formats • Application • Building Finder • Road Extraction • Etc.

  4. Data Sources • Microsoft Terraservice • Satellite Image • Feature Points • Feature name • Type • Lattitude/Longitude • TIGER/Line Files (A digital database of geographic features, such as roads, railroads, rivers, lakes, legal boundaries, census statistical boundaries, etc. covering the entire United States.) • Name • Type of feature • Latitude/Longitude • Address, etc…

  5. Data Sources • Online data / Yellow pages • Type • Name • Address White lines: Roads from TIGER/Line data source Image: MS Terraservice satellite image

  6. Finding Control Points • Control point pair consists of a point in one dataset and a corresponding point in the other dataset. • Determines accuracy of the algorithm. • Used to transform arbitrary points from one dataset to other. Methods: • Using Online Data • Analyzing Imagery Using Vector Data

  7. Control Points Using Online Data

  8. Control Points Using Online Data • Method • For a given location TerraService dataset has accurate control points (churches, libraries, hospitals, etc.) • Find the corresponding control points in Tiger/Lines dataset • Search landmark categories on yellow page sources • Get the address of the landmark find the address in Tiger/Lines DB • Match the names of the landmarks and find matching control points • Problems • Inaccuracies in yellow pages • Landmarks are not uniformly distributed • Landmarks may have large areas

  9. Control Points Using Online Data Terraservice DB Yellow pages, Tiger/Line DB integrated

  10. Control Points Analyzing Imagery Using Vector Data • Road intersections may be good control points • Use computer vision techniques to find the roads intersections on satellite image • Find intersections in Tiger/Line files • Match control points • Automatically extracting road intersections on large images are: • Time consuming • Inaccurate Proposed Method: Localized Image Processing

  11. Localized Image Processing • Mark the locations of the intersections points found from Tiger/Line DB on satellite image • Define the area size parameter • Start with a small area size, increase the area size until meet some clear features • Search the region centered at marked point having given area size • Find the edges on the given region • Mark the intersection of detected lines • Smaller search region • easier • faster

  12. FilteringControl Points • Both methods may generate inaccurate points • Inaccurate points reduce the accuracy of alignment of data sets • Inaccurate control points are detected by identifying pairs having significantly different relationship than the other pairs Vector Median Filter • Represent each control point pair by a 2D displacement vector • Median vector is the vector that has the least summed distance to other points • Finds the correct median if pairs are accurate • Modified to get the k nearest vectors to the median

  13. Vector Median Filter • As k increases provides more control points, but there may be more inaccurate pairs • A natural choice is to select

  14. Conflating Imagery And Vector Data • Arbitrary points on one of the data set is transferred to the other using the extracted control points • Delaunay Triangulation and piecewise linear rubber sheeting are utilized for transformation Triangulation • Alignment according to local adjustments is proposed • The domain is partitioned into small pieces (triangles) • Delaunay Triangulation is used • Maximizes the minimum angle of all the angles in the triangulation • Avoids triangles with small angles • Built in O(nlogn) time

  15. Conflating Imagery And Vector Data Piecewise Linear Rubber Sheeting • Find the transformation coefficients to map triangulation of vector data to imagery • Apply the same coefficients to the ends of road segments of vector data • Construct the road network on satellite image • Since Delaunay Triangulation avoids triangles with small angles, there is less distortion Region Growing • Used to find control points where there is no landmarks or intersections • Extrapolation on current control points is performed

  16. Performance Evaluation • Tests are performed integrating vector data to satellite imagery • Evaluation is performed according to generation of control points and effect of filtering • Hypothesis • Automated conflation using automatically generated control points without filtering improves accuracy of road identifications • Filtering technique further improves the results • Best results are achieved with localized image processing with vector filtering

  17. Experimental Setup • Microsoft TerraService web server was used to query satellite images • Tiger/Line files were used as the vector data • There are spatial inconsistencies between the data sets • Accurate roads are generated by conflating vector data with manually selected control point pairs • The experiments are performed by measuring the displacement between the conflated road endpoints and accurate road endpoints • Results are given for both control point generation method with/without filtering • Tests are performed on two different locations having 300/500 road end points

  18. Online Data vs. Intersection Points

  19. Filtered Vs. Unfiltered Control Points

  20. Results VMF Filtered Online Data VMF Filtered Intersection Pts.

  21. Conclusion • An automated integration approach is designed and implemented • Results show improvement on road identification • Does not offer a general mechanism • Accurate roads may be marked manually on the satellite image??? • Different transformations may be applied on arbitrary points???

  22. The End

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