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A matching approach focused on parallel roads and looping crosses in digital maps

A matching approach focused on parallel roads and looping crosses in digital maps. M. Zhang meng.zhang@bv.tum.de. Department of Cartography, TU Munich. ICA Symposium Wuhan, China October 28 ~ 29, 2006. 1. Introduction. What is data matching?

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A matching approach focused on parallel roads and looping crosses in digital maps

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  1. A matching approach focused on parallelroads and looping crosses in digital maps M. Zhang meng.zhang@bv.tum.de Department of Cartography, TU Munich ICA Symposium Wuhan, China October 28 ~ 29, 2006

  2. 1. Introduction • What is data matching? The process aims at establishing logical connections between corresponding objects in two comparable datasets is termed as data matching. • Why data matching? - To increase potentiality of the existing data - To maintain and update datasets in MRDB (Multiple Representation Database) - To evaluate and improve the data quality - To provide navigation solution for LBS (Location Based Service) - …

  3. 1. Introduction An approach is presented for the matching between street symbols of a digital map (e.g. Basis DLM) and corresponding objects (e.g. Tele Atlas) in a road dataset. The focus is put on two challenging cases that had not been considered in matching programs so far - parallel raods and looping crosses. Parallel lines (left); looping crosses (right). Red and blue represent two data sets to be matched

  4. 1. Introduction Problems • Map symbols (e.g. Basis DLM) seldom bear any useful semantic information • The looping crosses and parallel roads have quite different characteristics to each other, so it is not possible to commendably match both of them with the same criteria or algorithm. Solutions • A case-oriented matching approach is proposed, which can be characterized by three consecutive processes: (1) category recognition, (2) process modelling, and (3) process execution.

  5. 2. Category Recognition Category recognition is the activity to recognize objects or object clusters based on their spatial characteristics and assign them a category. Different categories will trigger different matching methods. • Category Recognition of looping crosses • Category recognition of parallel roads

  6. 3. Process Modelling - looping crosses Matching reference Matching candidate Identification of possible matching candidates

  7. 3. Process Modelling - looping crosses • Characteristic 1: Area • Characteristic 2: Position • Characteristic 3: Shape Calculation of the geometrical similarity

  8. 3. Process Modelling - looping crosses Exclusion of incorrect candidates

  9. 3. Process Modelling – parallel roads 1st step Preprocessing of the reference parallel roads The first polyline The second polyline • Proofreading the orientation of the reference parallel roads • Sequencing the reference parallel roads according to their relative position (ABDCA is in clockwise order).

  10. 3. Process Modelling – parallel roads 2nd step Matching of the first line (Polyline AB) Identification of the possible matching candidates of Polyline AB

  11. 3. Process Modelling – parallel roads 2nd step Matching of the first line (Polyline AB) Rejected Selection of the promising matching candidates of Polyline AB

  12. 3. Process Modelling – parallel roads 2nd Step Matching of the first line (Polyline AB) An example showing the limitation of geometric matching strategy

  13. 3. Process Modelling – parallel roads 2nd Step Matching of the first line (Polyline AB) Exactness inspection of the matching candidates of Polyline AB

  14. 3. Process Modelling – parallel roads 3rd Step Matching of the second line (Polyline CD)

  15. 3. Process Modelling – parallel roads d 4th Step Selection of the optimal combination A The combination is confirmed as the ultimate matching result

  16. 4. Process execution Process execution of parallel roads

  17. 5. Matching result Red lines: Tele Atlas Blue lines: Basis DLM Matching cases of looping crosses

  18. 5. Matching result 50 m Red lines: Tele Atlas Blue lines: Basis DLM Matching cases of parallel roads

  19. 6. Application – Integration of the post data Black lines: Basis DLM Red lines: Tele Atlas Yellow points: Post addresses Integration process of postal data

  20. 7. Conclusion • This paper proposes a case-oriented matching approach, which touches upon not only common street data but also the challenge cases of looping crosses and parallel roads. • This matching approach reveals high matching rate and accuracy andcan be applied for transfer of the information bound to one of the digital maps to another. • The case-oriented matching approach can be also utilized to deal with other special matching cases, such as short stubbles, narrow passages, slip roads around cloverleaf junctions etc. • We believe that a perfect matching approach is possible only if the context information can be holistically considered.

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