1. Linking objects of different spatial data sets by integration and Aggregation An article by Monika Sester, Karl-Heinrich Andres and Volker Walter Lecture by Gil Zellner

2. What is a map? wikipedia: A map is a visual representation of an area — a symbolic depiction highlighting relationships between elements of that space such as objects, regions, and themes.

3. What is a map (cont’d) A map is not just a 2d image: A map is not just a 2d image: List of objectsList of objects Partitions of areasPartitions of areas Linking informationLinking information Different versions of the same areaDifferent versions of the same area Aerial PhotoAerial Photo Satellite ImageSatellite Image

4. Outline The article discusses ways of integrating different maps onto a single easily accessible database, without losing information. The article discusses ways of integrating different maps onto a single easily accessible database, without losing information.

5. What is the problem with unification? Satellite images are not always available, often outdated, and more expensive. Satellite images are not always available, often outdated, and more expensive.

6. What is the problem with unification? (cont’d) Aerial photo limits Aerial photo limits Aerial reconnaissance photos are taken as “strips” of a larger whole.Aerial reconnaissance photos are taken as “strips” of a larger whole. Even the slightest (and with current technology, unavoidable) shift in angle, connecting them is difficultEven the slightest (and with current technology, unavoidable) shift in angle, connecting them is difficult

8. What is the problem with unification? (cont’d) Even if we still had all the data: Even if we still had all the data: Inaccuracies prevent us from matching objectsInaccuracies prevent us from matching objects Terrain is not flat, angle of photography…Terrain is not flat, angle of photography… Information is not AbsoluteInformation is not Absolute

9. Motivation Many maps today exist in many different formats, each containing : Many maps today exist in many different formats, each containing : some correlating informationsome correlating information some different informationsome different information The TRUE potential of this information is when it is integrated and we can see all of it at once … The TRUE potential of this information is when it is integrated and we can see all of it at once …

10. Motivation- examples Multi-national forces in IRAQ\Afghanistan use non-stanag equipment, which uses arcane map formats, maps are essential for efficient cooperation! Multi-national forces in IRAQ\Afghanistan use non-stanag equipment, which uses arcane map formats, maps are essential for efficient cooperation! - STANAG is a family of NATO standards for military equipment.

11. Motivation- examples (cont’d) Information from freely available maps on web sites can be used to see trends in demographics, economy etc … Information from freely available maps on web sites can be used to see trends in demographics, economy etc …

12. What is the closest chinese restaurant ? Motivation- examples (cont’d)

15. Problem Many formats exist, integrating them can be quite difficult without losing information Many formats exist, integrating them can be quite difficult without losing information DLM = digital landscape model Cadastre = bordered maps

16. Solution? Conversion into a single format ? Conversion into a single format ? Not a viable option, since data can become bloated and hard to decipher, also – some data STILL will be lost!

17. Solution – take 2 We keep all the original data, and simply link the objects together, choosing when to use one format or another. We keep all the original data, and simply link the objects together, choosing when to use one format or another. This article focuses on the linking aspects. This article focuses on the linking aspects.

18. Our formats GDF – specifically designed for road network data – vehicle navigation GDF – specifically designed for road network data – vehicle navigation

19. Our formats (cont’d) ATKIS – Topographic data system ATKIS – Topographic data system

20. Our formats (cont’d) Since the common data between system is roads, they are the matching primitives Since the common data between system is roads, they are the matching primitives

21. Matching at object level The usual system for matching information The usual system for matching information This is not possible here!

22. What is geometric matching ?

23. Matching at geometry level This we CAN do! This we CAN do!

24. The different Approaches

25. Examples of geometric matching

26. Matching examples (cont’d)

28. How do we efficiently match these objects ? Cardinality of the matching pairs Cardinality of the matching pairs

29. Efficient matching (cont’d) Normal Machine vision is clunky and difficult Solution: use noise margins, and Map the matching problem onto a communication system!

30. Noise margins

31. Matching problem mapped onto a communication system

32. Matching function

33. Matching function (cont’d) In order to calculate the mutual information I(D1,D2), In order to calculate the mutual information I(D1,D2), the 2 data sets are seen as messages which consist of symbols represented by our match primitives – the centerlines of streets. the 2 data sets are seen as messages which consist of symbols represented by our match primitives – the centerlines of streets.

34. Matching function (cont’d) For the matching of GDF and ATKIS data we take account the length, shape, and position of start and end points For the matching of GDF and ATKIS data we take account the length, shape, and position of start and end points

35. Matching function (cont’d) Our final function: Our final function:

36. Results

37. Medium scale object from large scale data through Aggregation Now that we know how to establish connections between objects of the same scale, we have another problem: Now that we know how to establish connections between objects of the same scale, we have another problem: Multi-scale data objects Multi-scale data objects

38. Multi scale data objects How do we match objects of different scale ? How do we match objects of different scale ? First we transform them to a similar scale (data aggregation problem)First we transform them to a similar scale (data aggregation problem)

41. Scaling

42. Our formats: German ALK (1:500) German ALK (1:500) ATKIS DLM25 (1:25000) ATKIS DLM25 (1:25000)

43. The process Classification Classification Based on usageBased on usage Relations are check by combinationRelations are check by combination Aggregation Aggregation Adjoining parcels are aggregatedAdjoining parcels are aggregated Separated areas are merged accordinglySeparated areas are merged accordingly

44. Learning Aggregation rules Usage of “typical” machine learning can be used here Usage of “typical” machine learning can be used here What to groupWhat to group Why groupWhy group When to groupWhen to group

45. Learning Objects and Semantic relations 1) Object Types 2) Classification is derived from the data set 3) Classes created

46. Learning Objects and Semantic relations (cont’d)

49. 1 st phase Classification

50. Final Classification

51. Structural Description of knowledge acquired

52. Summary Linkage of objects based on geometry Linkage of objects based on geometry Linkage of different scaled objects Linkage of different scaled objects

53. Article Criticism Lack of proper explanation Lack of proper explanation Not self contained Not self contained Addresses problems without proper explanation of “Train of thought” Addresses problems without proper explanation of “Train of thought”

54. Questions ?