pr esentation kadaster_final

3D Cadastre

McEnroe Gifford D’silvaPoojith N Jain

Motivation

Mr. SimonsMr. David

Mr. Toffel

Miss. GraceMr. Sharma

Mr. Jain

Owner : Mr. SimonsPrice : $ ******

Year Of Construction: 1998

Motivation

Miss. Grace

Mr. Sharma

Mr. David

Mr. Jain

Mr. Toffel

Mr. Simons

Mr. Simons

Aim of the project

• Feasibility study: Extension of CityGML• Extraction of information from building floor plans• Representing information in Extended CityGML

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.

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:LinearRing>

………….

</gml:CompositeSurface>

……………..

</lod2SolidProperty>

</Building>

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>

Features

• Modularisation• Representation of object surface characteristics

(textures, materials) • Multiscale model with 5 Levels of Detail (LOD)

Features

• Modularisation • Representation of object surface characteristics


Modularisation

Features

• Modularisation • Representation of object surface characteristics


Texturing

Features

• Modularisation• Representation of object surface characteristics


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)

Why CityGML ?

• Adds semantic and topological aspects into the models

• International standard

• Extendable

Extension of CityGML for Kadaster Purpose

Why extend CityGML ?

• No properties predefined for legal purpose

• Kadaster needs to model extra information

Extending CityGML

• Two methods for extending CityGML

Generic Objects/Attributes

Application Domain Extensions (ADE)

Generic Objects/Attributes

• Run time.• Key word Generic• Data types : String, Integer, Double, Date, URI• XML parser can not validate• Naming conflicts

Application Domain Extensions (ADE)

• Extra XML schema definition file• Explicitly imported• Own namespace• Validated by committee

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

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

Modeled Building

Front View

Added Attributes to Building

Extended Properties to Building

Added Attributes to Apartment

Recognition system for the building floor plans

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>

Basic Concepts

• Computer representation of images• Pixels• Pixel value based on the color• Array representation

The Process

Image Pre-processing Data Reduction

Graph Construction

CityGML

Building Floor Plans

• Gray scale image• High Resolution

Thick lines ownership boundaryNumbers ownership rightsTexts usage type

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

The Process

Image Pre-processing

Data Reduction

Graph Construction

CityGML

Thresholding and Noise Removal

• Thresholding• Noise

• Gaps • Missing pixels

• Continuity is important for contour detection

• Solution• Closing Operation

Closing Operation

Input Image

Structuring Element

The Process

Removing Texts and

Thin Lines

Number Identification


Data Reduction

Graph Construction

CityGML

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

The Process

Removing Texts and

Thin Lines

Number Identification


Data Reduction

Graph Construction

CityGML

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

Opening Operation

• Opening

The Process


Data Reduction

Graph Construction

CityGML

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.

The Process

Corner Detection

Graph Construction

Skeletonization

Image Pre Processing

Data Reduction

Graph Construction

Face and Floor

Identification

CityGML

Skeletonization

• Why Skeletonization?• Reduces foreground

regions in an image to a skeleton

• Skeleton should be• One pixel width• Preserves connectivity• Preserves Topology• Centered

The Process

Corner Detection

Graph Construction

Face and Floor

Identification

Skeletonization


Data Reduction

Graph Construction

CityGML

Corner Detection

• Corners are intersection of two or more edges

• Corners form the nodes of the graph

• Harris Corner DetectionCorner

Detection

Graph Construction

• Identify the nodes• Identify the edges• Optimization

The Process

Corner Detection

Graph Construction

Skeletonization


Data Reduction

Graph Construction

CityGML

Face and Floor

Identification

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

…

Floor Identification

• Identifying Floors• Storing Information

2 3 4 44

1 2 3 4

OwnershipOwnership Right

Floor Number

Co-ordinates

. . .

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

Representation of Apartment Rights in CityGML format.

Overall Process

CityGML Representation

ProcessInput File

Output(Extended CityGML)

Input File

Ownership

Ownership Right

Floor Number

Co-ordinates

. . .

Height

Width

...

Steps involved:

Identification of unique OwnershipRights and unique Floor Number


Separation of objects into OwnershipRights, Floor Number and Regions


Example

2 1

Floor 1

1

Floor 2

Steps involved:





Grouping of regions with same OwnershipRightsGrouping of regions with same OwnershipRights

Advantages

• Use of any CityGML Viewer

• No plugins

Steps involved:






Transformation/translation of co-ordinates based on Floor Number


Translation based on floor number

Steps involved:








Representation in CityGML formatRepresentation in CityGML format

2D Output

Steps involved:








Representation in CityGML formatRepresentation in CityGML format

Converting the 2D model into a 3D modelConverting the 2D model into a 3D model

Final Output

Summary

• Extended CityGML can be used for cadastre purpose.

• Building floor plans can be effectively digitized and can be represented in CityGML.

PROJECT DEMONSTRATION

THANK YOU

pr esentation kadaster_final

Documents

lod lod

citygml file

materials multiscale

d city models

region lod

projects lod

landmarks lod

landscape lod