The document discusses extending the CityGML data model to represent 3D cadastre information. It identifies key properties needed for legal ownership purposes that are not predefined in CityGML, such as apartment numbers and owners. Two methods for extending CityGML - generic objects/attributes and application domain extensions - are described. An extended CityGML structure is proposed with additional attributes added to buildings and a new apartment feature class. The document also summarizes a process for recognizing information like ownership boundaries and numbers from 2D building floor plans and representing it in an extended CityGML format to generate a 3D cadastre model.

1. 3D Cadastre McEnroe Gifford D’silva Poojith N Jain

2. Motivation Owner : Mr. Simon Price : $ ****** Year Of Construction: 1998 Miss. Grace Mr. Sharma Mr. Simons Mr. David Mr. Jain Mr. Toffel

3. Motivation Owner : Mr. Simons Price : $ ****** Year Of Construction: 1998 Miss. Grace Mr. Sharma Mr. Simons Mr. David Mr. Jain Mr. Toffel

4. Motivation Mr. Simons Mr. Simons Miss. Grace Mr. Sharma Mr. David Mr. Jain Mr. Toffel

5. Motivation

6. Aim of the project Feasibility study: Extension of CityGML Extraction of information from building floor plans Representing information in Extended CityGML

7. CityGML Semantic information model for the representing 3D urban objects. An open data model and XML-based format for the storage and exchange of virtual 3D city models. Implemented as an application schema of the Geography Markup Language 3 (GML3). Classes and relations defined with respect to geometrical, topological,semantical, and appearance properties.

8. CityGML Semantic information model for representing 3D urban objects Open data model and XML-based format Implemented as an application schema of the Geography Markup Language 3 (GML3) Geometrical, topological,semantical, and appearance properties.

9. Building in CityGML 1 <Building gml:id=“Building0815”>… <lod2SolidProperty> <gml:SolidsrsName=“urn:adv:crs:ETR2-h”> <gml:exterior> <gml:CompositeSurface> <gml:surfaceMember> <gml:Polygon> <gml:exterior> <gml:LinearRing> <gml:pos>1.0 1.0 0.0</gml:pos> <gml:pos>3.0 1.0 0.0</gml:pos> ……………….. <gml:pos>1.0 1.0 0.0</gml:pos> </gml:LinearRing> …………. </gml:CompositeSurface> …………….. </lod2SolidProperty> </Building>

10. Building in CityGML 2 <Building gml:id=“Building0815”>… <lod2SolidProperty> <gml:SolidsrsName=“urn:adv:crs:ETR2-h”> <gml:exterior> <gml:CompositeSurface> <gml:surfaceMember> //front surface </gml:surfaceMember> <gml:surfaceMember> //side surface </gml:surfaceMember> //here comes side, back, roof and ground surfaces </gml:CompositeSurface> </gml:exterior> </gml:Solid> </lod2SolidProperty> </Building>

11. Features Modularisation Representation of object surface characteristics (textures, materials) Multiscale model with 5 Levels of Detail (LOD)

12. Features Modularisation Representation of object surface characteristics (textures, materials) Multiscale model with 5 Levels of Detail (LOD)

13. Modularisation

14. Features Modularisation Representation of object surface characteristics (textures, materials) Multiscale model with 5 Levels of Detail (LOD)

15. Texturing

16. Features Modularisation Representation of object surface characteristics (textures, materials) Multiscale model with 5 Levels of Detail (LOD)

17. 5 Levels of Detail (LOD) LOD 0 – regional, landscape LOD 1 – city, region LOD 2 – city districts, projects LOD 3 – architectural models (outside), landmarks LOD 4 – architectural models (interior)

18. Why CityGML ? Usual 3D city models have been defined as purely graphical or geometrical models. (mainly for Visualization). CityGML adds semantic and topological aspects into the models (queries, data mining). International standard. Extendable.

19. Why CityGML ? Adds semantic and topological aspects into the models International standard Extendable

20. Extension of CityGML for Kadaster Purpose

21. Why extend CityGML ? No properties predefined for legal purpose Kadaster needs to model extra information

24. ApplicationDomainExtensions (ADE) Extra XML schema definition file Explicitly imported Own namespace Validated by committee

25. Identified Properties

26. Identified Properties <<Feature>> _KadasterApartment <<Feature>> _AbstractBuilding + kad :: apartmentNumber[1] : xs : string + kad :: apartmentOwner [1..*] : xs :: string + kad :: ownership [1] : xs :: OwnershipType + kad :: apartmentInhabitants [1] : xs :: positiveInteger + kad :: roomCount [1] : xs :: positiveInteger + kad :: detachedRoom [1] : xs :: boolean + kad :: detachedRoomCount [1] : xs :: positiveInteger + kad :: parcelNumber [1] : xs :: string + kad :: buildingOwner [1...*] : xs :: string + kad :: buildingInhabitants [1] : xs :: positiveInteger + kad :: buildingApartment [1] : xs :: positiveInteger + kad :: buildingNumber [1] : xs :: string + kad :: buildingType [1] : kad :: BuildingType <<External CodeList>> BuildingType Type <<External CodeList>> OwnershipType Type

27. Proposed CityGML Structure <<Feature> Site_ <<Feature>> _CityObject <<Feature> Rooms <<Feature>> _AbstractBuilding Pre – defined Attributes +class : BuildingClassType [0..1] +function : BuildingFunctionType [0..*] +usage : BuildingUsageType [0..*] +yearOfConstruction : xs :: gYear [0..1] +yearOfDemolition : xs :: gYear [0..1] +roofType : RoofTypeType [0..1] +measuredHeight : gml :: LengthType [0..1] +storeysAboveGround : xs :: NonNegativeIntegers [0..1] +storeysBelowGround : xs :: NonNegativeIntegers [0..1] +storeysHeightAboveGround : xs :: MeasureOrNullListType [0..1] +storeysHeightBelowGround : xs :: MeasureOrNullListType [0..1] <<Feature>> _KadasterApartment + kad :: parcelNumber [1] : xs :: string + kad :: buildingOwner [1...*] : xs :: string + kad :: buildingInhabitants [1] : xs :: positiveInteger + kad :: buildingApartment [1] : xs :: positiveInteger + kad :: buildingNumber [1] : xs :: string + kad :: buildingType [1] : kad :: BuildingType + kad :: apartmentNumber[1] : xs : string + kad :: apartmentOwner [1..*] : xs :: string + kad :: ownership [1] : xs :: OwnershipType + kad :: apartmentInhabitants [1] : xs :: positiveInteger + kad :: roomCount [1] : xs :: positiveInteger + kad :: detachedRoom [1] : xs :: boolean + kad :: detachedRoomCount [1] : xs :: positiveInteger

28. Modeled Building

29. Front View

30. Added Attributes to Building

31. Extended Properties to Building

32. Added Attributes to Apartment

33. Recognition system for the building floor plans

34. Introduction Aim Extraction of the information Representation of the information in CityGML <CityObjectMember> <Polygon> <PosList> x1 y1 z1 x2 y2 z2 . . . </PosList> </Polygon> <CityObjectMember> 3 4

35. BasicConcepts Computer representation of images Pixels Pixel valuebasedon the color Arrayrepresentation

36. The Process Image Pre-processing Data Reduction Graph Construction CityGML

38. Numbersownershiprights

40. The Process Image Pre-processing Data Reduction Graph Construction CityGML

41. Thresholding and NoiseRemoval Thresholding Noise Gaps Missing pixels Continuity is important for contour detection Solution ClosingOperation

42. ClosingOperation Structuring Element Input Image

43. The Process Pre-processing Data Reduction Graph Construction CityGML Image NumberIdentification RemovingTexts and Thin Lines

45. The Process Pre-processing Data Reduction Graph Construction CityGML Image NumberIdentification RemovingTexts and Thin Lines

46. RemovingTexts and Thin Lines Textsindicatepropertyusage and type Thinlinesindicate sub regioninformation Removetexts and thinlines. Connected component labeling Opening operation

48. Graph Graph is an ordered pair G: = (V,E) comprising a set V of vertices together with a set E of edges. Graph is used to show connectivity of vertices.

49. The Process Pre Processing Data Reduction Graph Construction CityGML Image Skeletonization Corner Detection Graph Construction Face and Floor Identification

50. Skeletonization WhySkeletonization? Reducesforegroundregions in an image to a skeleton Skeletonshouldbe One pixel width Preserves connectivity Preserves Topology Centered

51. The Process Pre-processing Data Reduction Graph Construction CityGML Image Skeletonization Corner Detection Graph Construction Face and Floor Identification

52. Corner Detection Corners are intersection of twoor more edges Corners form the nodes of the graph Harris Corner Detection Corner Detection

53. Graph Construction Identify the nodes Identify the edges Optimization

54. Graph Construction Identify the nodes Identify the edges Optimization

55. The Process Pre-processing Data Reduction Graph Construction CityGML Image Skeletonization Corner Detection Graph Construction Face and Floor Identification

56. Face Recognition Eachenclosed face becomesownershipboundary Associateownership Store the information {3,x,y} 3 {4,x,y} 4

57. Floor Identification IdentifyingFloors Storing Information 2 3 4 4 4 1 2 3 4

58. Storing the information <OwnershipRights> <Object> <floorNumber> 1 </floorNumber> <OwnershipRight> 3 </OwnershipRight> <Polygon> x1 y1 z1 x2 y2 z2 . . . </Polygon> </Object> <Object> . . . <Object> </OwnershipRights> 3 4 1

59. Representation of Apartment Rights in CityGML format.

60. Overall Process CityGML Representation Process Input File Output (Extended CityGML)

61. Input File

62. Input File

63. Input File

64. Steps involved: Separation of objects into ApartmentRights, FloorCount and Regions. Identification of unique ApartmentRights and unique FloorCount. Parcel ? Transformation/translation of co-ordinates based on FloorCount. Add OwnershipRights Grouping of regions with same ApartmentRights Representation in CityGML format. Converting the 2D map into a 3D City Model

65. Steps involved: Separation of objects into OwnershipRights, Floor Number and Regions Identification of unique OwnershipRightsand unique Floor Number

66. Example 2 1 1 Floor 1 Floor 2

67. Steps involved: Separation of objects into OwnershipRights, Floor Number and Regions Identification of unique OwnershipRightsand unique Floor Number Grouping of regions with same OwnershipRights

68. Advantages Use of any CityGML Viewer No plugins

69. Steps involved: Separation of objects into OwnershipRights, Floor Number and Regions Identification of unique OwnershipRightsand unique Floor Number Grouping of regions with same OwnershipRights Transformation/translation of co-ordinates based on Floor Number

70. Translation based on floor number

71. Translation based on floor number

72. Steps involved: Separation of objects into OwnershipRights, Floor Number and Regions Identification of unique OwnershipRightsand unique Floor Number Grouping of regions with same OwnershipRights Transformation/translation of co-ordinates based on Floor Number Representation in CityGMLformat

73. 2D Output

74. Steps involved: Separation of objects into OwnershipRights, Floor Number and Regions Identification of unique OwnershipRightsand unique Floor Number Grouping of regions with same OwnershipRights Transformation/translation of co-ordinates based on Floor Number Representation in CityGMLformat Converting the 2D model into a 3D model

75. Steps involved: Separation of objects into ApartmentRights, Floor Number and Regions Identification of unique ApartmentRights and unique Floor Number Transformation/translation of co-ordinates based on Floor Number Grouping of regions with same ApartmentRights Representation in CityGMLformat Converting the 2D map into a 3D City Model

76. Final Output

77. Summary Extended CityGML can be used for cadastre purpose. Building floor plans can be effectively digitized and can be represented in CityGML.

78. PROJECT DEMONSTRATION Floor 1 Floor 2

79. PROJECT DEMONSTRATION Floor 1 Floor 2 Stacked Output

80. PROJECT DEMONSTRATION

81. THANK YOU