On Constructing a Base Map for Collaborative Map Generation and its Application in Urban Mobility Planning

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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

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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.

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Challenges and Solutions

• Combining the GPS traces collected by people.
• Through Computational Geometry algorithms
• Shahid Jabbar, Masters 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, ITSC05
• 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)

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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.

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Extraction of Road Surfaces

• Streets extraction by color values.

• Problem Railway tracks and street names are
  also black!

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Erosion

• Street names and railway tracks are
  eliminated.

3x3 Mask
















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Dilatation

• Street lines might become distorted by
  erosion ? Made thicker again.
• Small holes due to street names are filled

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Other Filters

• Morphological Opening and Closing
• Gap closing
• Fragment Elimination

• Smoothening of contours

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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.

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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

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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

e (epsilon) as the accuracy parameter
(x3,y3)
(x2,y2)
(x1,y1)
If d 0, (x2,y2) can be deleted!
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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
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Integration with SUMO
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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.

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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)

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Thank You!Questions ?