Automatic and accurate extraction of road intersections from raster maps

Yao Yi Chiang, Craig A. Knoblock, Cyrus Shahabi, Ching Chien Chen

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

Original languageEnglish (US)
Pages (from-to)121-157
Number of pages37
Issue number2
StatePublished - 2009
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This research is based upon work supported in part by the United States Air Force under contract number FA9550-08-C-0010, in part by the National Science Foundation under Award No. IIS-0324955, in part by the Air Force Office of Scientific Research under grant number FA9550-07-1-0416, in part by a gift from Microsoft, 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. We would like to thank Dr. Chew Lim Tan for his generous sharing of their code in [6].


  • Conflation
  • Fusion
  • Geospatial data integration
  • Imagery
  • Raster map
  • Road intersection
  • Road layer
  • Vector data


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