Abstract
As an alternative to expensive road surveys, we are working toward a method to infer the road network from GPS data logged from regular vehicles. One of the most important components of this problem is to find road intersections. We introduce an intersection detector that uses a localized shape descriptor to represent the distribution of GPS traces around a point. A classifier is trained on the shape descriptor to discriminate intersections from non-intersections, and we demonstrate its effectiveness with an ROC curve. In a second step, we use the GPS data to prune the detected intersections and connect them with geometrically accurate road segments. In the final step, we use the iterative closest point algorithm to more accurately localize the position of each intersection. We train and test our method on GPS data gathered from regular vehicles in the Seattle, WA, USA area. The tests show we can correctly find road intersections.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Haklay, M., Weber, P.: OpenStreetMap: User-Generated Street Maps. In: IEEE Pervasive Computing, pp. 12–18 (2008)
Tavakoli, M., Rosenfeld, A.: Building and Road Extraction from Aerial Photographs. IEEE Transactions on Systems, Man, and Cybernetics SMC-12(1), 84–91 (1982)
Hu, J., et al.: Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints. IEEE Transactions on Geoscience and Remote Sensing 45(12), 4144–4157 (2007)
Barsia, A., Heipkeb, C.: Artificial Neural Networks for the Detection of Road Junctions in Aerial Images. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Munich, Germany, pp. 113–118 (2003)
Rogers, S., Langley, P., Wilson, C.: Mining GPS Data to Augment Road Models. In: International Conference on Knowledge Discovery and Data Mining, pp. 104–113 (1999)
Guo, T., Iwamura, K., Koga, M.: Towards High Accuracy Road Maps Generation from Massive GPS Traces Data. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 667–670 (2007)
Bruntrup, R., et al.: Incremental Map Generation with GPS Traces. IEEE Intelligent Transportation Systems, 574–579 (2005)
Worrall, S., Nebot, E.: Automated Process for Generating Digitised Maps through GPS Data Compression. In: Australasian Conference on Robotics and Automation, Brisbane, Australia (2007)
Schroedl, S., et al.: Mining GPS Traces for Map Refinement. Data Mining and Knowledge Discovery 9(1), 59–87 (2004)
Edelkamp, S., Schrödl, S.: Route Planning and Map Inference with Global Positioning Traces. In: Klein, R., Six, H.-W., Wegner, L. (eds.) Computer Science in Perspective. LNCS, vol. 2598, pp. 128–151. Springer, Heidelberg (2003)
Cao, L., Krumm, J.: From GPS Traces to a Routable Road Map. In: 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPAITLA GIS 2009), pp. 3–12. ACM, Seattle (2009)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Computer Vision and Pattern Recognition (CVPR 2001), p I-511– I-518 (2001)
Besl, P.J., McCay, N.D.: A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fathi, A., Krumm, J. (2010). Detecting Road Intersections from GPS Traces. In: Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C. (eds) Geographic Information Science. GIScience 2010. Lecture Notes in Computer Science, vol 6292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15300-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-15300-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15299-3
Online ISBN: 978-3-642-15300-6
eBook Packages: Computer ScienceComputer Science (R0)