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Automatically and Accurately Conflating Raster Maps with Orthoimagery

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Abstract

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.

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Notes

  1. http://seamless.usgs.gov

  2. http://arcweb.esri.com/sc/viewer/index.html

  3. http://www.mapquest.com

  4. http://maps.google.com/

  5. http://terraserver-usa.com/

  6. http://www.census.gov/geo/www/tiger/

  7. A map intersection is characterized as an accurately detected point if and only if its location is less than five pixels from the position of the actual map intersection. For single line map, the actual position of an intersection is the point where the associated road segments meet. For double line map, the points that fall within the polygons formed by the elongated road regions are considered as actual intersections.

  8. We can determine the map resolution for a raster map from the known map scale.

  9. We removed the background imagery and road networks in order to clearly display the distribution of points on the imagery.

  10. The matching point pairs notation ((m i, s i),(m j, s j)) implies that m i matches s i and m j matches s j.

  11. ESRI provides various online map services. In order to evaluate our proposed map-imagery conflation technique for maps with unknown map scale, we used the ESRI maps available at http://arcweb.esri.com/sc/viewer/index.html in the experiments. Neither map scale nor geocoordinates of ERSI maps are provided from this web site.

  12. http://www.mapquest.com

  13. http://maps.yahoo.com/

  14. http://terraserver-usa.com/

  15. MO-DOT is the road network data provided by the Missouri Department of Transportation. It is high quality vector data with highly accurate road geometry.

  16. Note that if the shapes of corresponding road segments in the set MRR and MCR are different, the lengths of these corresponding road segments may be different.

  17. The experiment platform was a Pentium III 1.2 GHz processor with 512 MB memory running Windows XP (with .NET framework installed).

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Acknowledgements

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.

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Correspondence to Ching-Chien Chen.

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This work is based on an earlier work: Automatically and accurately conflating orthoimagery and street maps, in the Proceedings of the 12th Annual ACM International Symposium on Advances in Geographic Information Systems, {2004} © ACM, 2004. http://doi.acm.org/10.1145/1032222.1032231.

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Chen, CC., Knoblock, C.A. & Shahabi, C. Automatically and Accurately Conflating Raster Maps with Orthoimagery. Geoinformatica 12, 377–410 (2008). https://doi.org/10.1007/s10707-007-0033-0

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