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Maik Drodzynski, Stefan Edelkamp, Andreas Gaubatz, Shahid Jabbar , and Miguel Liebe

On Constructing a Base Map for Collaborative Map Generation and its Application in Urban Mobility Planning. Maik Drodzynski, Stefan Edelkamp, Andreas Gaubatz, Shahid Jabbar , and Miguel Liebe Chair for Programming Systems, University of Dortmund, Germany. Motivation. Problem:

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Maik Drodzynski, Stefan Edelkamp, Andreas Gaubatz, Shahid Jabbar , and Miguel Liebe

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  1. On Constructing a Base Map for Collaborative Map Generation and its Application in Urban Mobility Planning Maik Drodzynski, Stefan Edelkamp, Andreas Gaubatz, Shahid Jabbar, and Miguel Liebe Chair for Programming Systems, University of Dortmund, Germany

  2. Motivation • Problem: • Computer assisted urban mobility planning requires good vector maps. • Good vector maps are not always available, especially for many third world countries. • Solution: • Web 2.0 • Collaborative map generation • GPS-Tracks, • Wikimapia, • Open Street Map, etc. Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  3. Challenges and Solutions • Combining the GPS traces collected by people. • Through Computational Geometry algorithms • [Shahid Jabbar, Master’s Thesis, University of Freiburg, Germany, 2003] • [Edelkamp, Jabbar, Willhalm, ITSC 2003] • [Edelkamp, Jabbar, Willhalm, IEEE Transactions on ITS vol. 6 no. 1 (2005)] • AI clustering methods to combine these traces in order to infer road geometry • [Brüntrup, Edelkamp, Jabbar, Scholz, ITSC’05] • A reliable integration of traces require a good base map that can act as the template. • This paper discusses our approach to generate such a vector base map. • Borrows several techniques from Digital Image Processing and Computational Geometry. • Extracts calibrated road topology from raster maps. • Integrated with SUMO (by German Aerospace Agency, DLR) Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  4. Raster Maps • Can be collected easily from city authorities or through scanning paper maps. • A 2D arrangement of pixels. • Raster Maps from Dortmund, Germany. • Collected from the City authority of Dortmund. Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  5. Extraction of Road Surfaces • Streets’ extraction by color values. • Problem:Railway tracks and street names are also black! Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  6. Erosion • Street names and railway tracks are eliminated. 3x3 Mask Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  7. Dilatation • Street lines might become distorted by erosion  Made thicker again. • Small holes due to street names are filled Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  8. Other Filters • Morphological Opening and Closing • Gap closing • Fragment Elimination • Smoothening of contours Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  9. Road Skeleton Computation • Skeleton of a Pixel Map: A set of thin curves denoting the centerlines of the black surfaces. • Medial Axis Transformation • Extraction of the center lines of the thick surfaces. Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  10. Graph Construction • Sweep-line paradigm:process pixels in columns • For each crossing, start a traversal in all possible directions! • Need a hash table to avoid duplicate work Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  11. Graph Simplification • Several thousands of nodes are generated! • Not all are required or – more precisely – “interesting”. • Employ a similar algorithm as Douglas-Peucker simplification. • Co-linearity test ε (epsilon) as the accuracy parameter (x3,y3) (x2,y2) (x1,y1) If d = 0, (x2,y2) can be deleted! Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  12. Raster Maps Raster to Vector Transformation SUMO – Simulation for Urban Mobility (by DLR) • A start-of-the-art tool for traffic simulation • Used during FIFA-06 and Catholic Youth day, along with a Zeppelin to give real-time guidance to the traffic authority. Nodes + Edges in XML Simulation Results SUMO Routes Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  13. Integration with SUMO Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  14. Summary • Urban mobility planning require a good vector map. • Collaborative map generation needs a base map to correct the inaccuracies that can be added by people. • Raster maps are inexpensive and widely available. • Good quality maps can be obtained from the city authority. • We propose: • Extract a vector map from a raster map. • Digital Image Processing techniques can be helpful. • Integrated with SUMO – a state-of-the-art tool for traffic simulation. Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  15. Future extensions • Better image processing for Bridges – 3D. • Integration with lane information. • Traffic Signals etc. • Special Thanks to:Daniel Krajzewicz at German Aerospace Agency (DLR) Drodzynski, Edelkamp, Gaubatz, Jabbar & Liebe

  16. Thank You!Questions ?

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