pr esentation kadaster_final
TRANSCRIPT
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3D Cadastre
McEnroe Gifford D’silvaPoojith N Jain
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Motivation
Mr. SimonsMr. David
Mr. Toffel
Miss. GraceMr. Sharma
Mr. Jain
Owner : Mr. SimonsPrice : $ ******
Year Of Construction: 1998
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Motivation
Miss. Grace
Mr. Sharma
Mr. David
Mr. Jain
Mr. Toffel
Mr. Simons
Mr. Simons
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Aim of the project
• Feasibility study: Extension of CityGML• Extraction of information from building floor plans• Representing information in Extended CityGML
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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.
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Building in CityGML 1
<Building gml:id=“Building0815”>…
<lod2SolidProperty>
<gml:Solid srsName=“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>
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Building in CityGML 2
<Building gml:id=“Building0815”>…
<lod2SolidProperty>
<gml:Solid srsName=“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>
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Features
• Modularisation• Representation of object surface characteristics
(textures, materials) • Multiscale model with 5 Levels of Detail (LOD)
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Features
• Modularisation • Representation of object surface characteristics
(textures, materials) • Multiscale model with 5 Levels of Detail (LOD)
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Modularisation
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Features
• Modularisation • Representation of object surface characteristics
(textures, materials) • Multiscale model with 5 Levels of Detail (LOD)
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Texturing
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Features
• Modularisation• Representation of object surface characteristics
(textures, materials) • Multiscale model with 5 Levels of Detail (LOD)
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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)
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Why CityGML ?
• Adds semantic and topological aspects into the models
• International standard
• Extendable
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Extension of CityGML for Kadaster Purpose
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Why extend CityGML ?
• No properties predefined for legal purpose
• Kadaster needs to model extra information
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Extending CityGML
• Two methods for extending CityGML
Generic Objects/Attributes
Application Domain Extensions (ADE)
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Generic Objects/Attributes
• Run time.• Key word Generic• Data types : String, Integer, Double, Date, URI• XML parser can not validate• Naming conflicts
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Application Domain Extensions (ADE)
• Extra XML schema definition file• Explicitly imported• Own namespace• Validated by committee
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Identified Properties
<<Feature>>_KadasterApartment
+ 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
<<Feature>>_AbstractBuilding
+ 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
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Proposed CityGML Structure
<<Feature>>_CityObject
<<Feature>Site_
<<Feature>>_KadasterApartment
+ 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
Pre – defined Attributes
+ 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
<<Feature>>_AbstractBuilding
+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>Rooms
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Modeled Building
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Front View
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Added Attributes to Building
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Extended Properties to Building
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Added Attributes to Apartment
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Recognition system for the building floor plans
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Introduction
• Aim• Extraction of the information• Representation of the information in CityGML
3
4
<CityObjectMember> <Polygon> <PosList> x1 y1 z1
x2 y2 z2. . . </PosList> </Polygon><CityObjectMember>
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Basic Concepts
• Computer representation of images• Pixels• Pixel value based on the color• Array representation
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The Process
Image Pre-processing Data Reduction
Graph Construction
CityGML
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Building Floor Plans
• Gray scale image• High Resolution
Thick lines ownership boundaryNumbers ownership rightsTexts usage type
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Assumptions made
• Thick lines indicate ownership boundary• Numbers enclosed in a polygon• Single number in a polygon• Numbers do not overlap with the lines and the
symbols
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The Process
Image Pre-processing
Data Reduction
Graph Construction
CityGML
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Thresholding and Noise Removal
• Thresholding• Noise
• Gaps • Missing pixels
• Continuity is important for contour detection
• Solution• Closing Operation
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Closing Operation
Input Image
Structuring Element
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The Process
Removing Texts and
Thin Lines
Number Identification
Image Pre-processing
Data Reduction
Graph Construction
CityGML
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Ownership Identification
• Identify the location of the numbers
• Extract the numbers• Recognize numbers
OCR
OCR
{3,x,y}
{4,x,y}
1 11 1 1 2
1 1 2 2 21 2 2 2
3 3 3 4 43 3 3 4 4
• Connected component labeling
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The Process
Removing Texts and
Thin Lines
Number Identification
Image Pre-processing
Data Reduction
Graph Construction
CityGML
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Removing Texts and Thin Lines
• Texts indicate property usage and type
• Thin lines indicate sub region information
• Remove texts and thin lines.• Connected component
labeling• Opening operation
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Opening Operation
• Opening
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The Process
Image Pre-processing
Data Reduction
Graph Construction
CityGML
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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.
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The Process
Corner Detection
Graph Construction
Skeletonization
Image Pre Processing
Data Reduction
Graph Construction
Face and Floor
Identification
CityGML
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Skeletonization
• Why Skeletonization?• Reduces foreground
regions in an image to a skeleton
• Skeleton should be• One pixel width• Preserves connectivity• Preserves Topology• Centered
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The Process
Corner Detection
Graph Construction
Face and Floor
Identification
Skeletonization
Image Pre-processing
Data Reduction
Graph Construction
CityGML
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Corner Detection
• Corners are intersection of two or more edges
• Corners form the nodes of the graph
• Harris Corner DetectionCorner
Detection
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Graph Construction
• Identify the nodes• Identify the edges• Optimization
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Graph Construction
• Identify the nodes• Identify the edges• Optimization
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The Process
Corner Detection
Graph Construction
Skeletonization
Image Pre-processing
Data Reduction
Graph Construction
CityGML
Face and Floor
Identification
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Face Recognition
• Each enclosed face becomes ownership boundary
• Associate ownership• Store the information
{3,x,y}
{4,x,y}
3
4Ownership
Ownership Right
Point co-ordinate
…
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Floor Identification
• Identifying Floors• Storing Information
2 3 4 44
1 2 3 4
OwnershipOwnership Right
Floor Number
Co-ordinates
. . .
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Storing the information
3
4
<OwnershipRights><Object>
<floorNumber> 1 </floorNumber><OwnershipRight> 3 </OwnershipRight><Polygon>
x1 y1 z1x2 y2 z2
. . .</Polygon></Object><Object> . . .<Object></OwnershipRights>
1
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Representation of Apartment Rights in CityGML format.
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Overall Process
CityGML Representation
ProcessInput File
Output(Extended CityGML)
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Input File
Ownership
Ownership Right
Floor Number
Co-ordinates
. . .
Height
Width
...
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Steps involved:
Identification of unique OwnershipRights and unique Floor Number
Identification of unique OwnershipRights and unique Floor Number
Separation of objects into OwnershipRights, Floor Number and Regions
Separation of objects into OwnershipRights, Floor Number and Regions
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Example
2 1
Floor 1
1
Floor 2
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Steps involved:
Identification of unique OwnershipRights and unique Floor Number
Identification of unique OwnershipRights and unique Floor Number
Separation of objects into OwnershipRights, Floor Number and Regions
Separation of objects into OwnershipRights, Floor Number and Regions
Grouping of regions with same OwnershipRightsGrouping of regions with same OwnershipRights
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Advantages
• Use of any CityGML Viewer
• No plugins
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Steps involved:
Identification of unique OwnershipRights and unique Floor Number
Identification of unique OwnershipRights and unique Floor Number
Separation of objects into OwnershipRights, Floor Number and Regions
Separation of objects into OwnershipRights, Floor Number and Regions
Grouping of regions with same OwnershipRightsGrouping of regions with same OwnershipRights
Transformation/translation of co-ordinates based on Floor Number
Transformation/translation of co-ordinates based on Floor Number
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Translation based on floor number
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Steps involved:
Identification of unique OwnershipRights and unique Floor Number
Identification of unique OwnershipRights and unique Floor Number
Separation of objects into OwnershipRights, Floor Number and Regions
Separation of objects into OwnershipRights, Floor Number and Regions
Grouping of regions with same OwnershipRightsGrouping of regions with same OwnershipRights
Transformation/translation of co-ordinates based on Floor Number
Transformation/translation of co-ordinates based on Floor Number
Representation in CityGML formatRepresentation in CityGML format
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2D Output
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Steps involved:
Identification of unique OwnershipRights and unique Floor Number
Identification of unique OwnershipRights and unique Floor Number
Separation of objects into OwnershipRights, Floor Number and Regions
Separation of objects into OwnershipRights, Floor Number and Regions
Grouping of regions with same OwnershipRightsGrouping of regions with same OwnershipRights
Transformation/translation of co-ordinates based on Floor Number
Transformation/translation of co-ordinates based on Floor Number
Representation in CityGML formatRepresentation in CityGML format
Converting the 2D model into a 3D modelConverting the 2D model into a 3D model
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Final Output
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Summary
• Extended CityGML can be used for cadastre purpose.
• Building floor plans can be effectively digitized and can be represented in CityGML.
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PROJECT DEMONSTRATION
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THANK YOU