3D GIS Modeling at Semantic Level using CityGML for … · vi Declaration I, Parag Sudhir Wate, hereby declare that this dissertation entitled “3D GIS Modeling at Semantic Level

3D GIS Modeling at Semantic Level using CityGML for

Urban Segment

Thesis submitted to the Andhra University, Visakhapatnam in partial fulfilment of

requirement for the award of Master of Technology in Remote Sensing and GIS

Submitted By:

Parag Sudhir Wate

Supervised By:

Dr. Sameer Saran

Scientist SE

Geoinformatics Department

Indian Institute of Remote Sensing, ISRO,

Dept. of Space, Govt. of India,

Dehradun – 248001

Uttarakhand, India

August, 2014

ii

DISCLAIMER

This work has been carried out in partial fulfillment of Masters of Technology program in

Remote Sensing and Geographic Information System at Indian Institute of Remote Sensing,

Dehradun, India. The author is solely responsible for the contents of the thesis.

iii

Acknowledgement

The presented research is part of my M. Tech. Project work that has been carried out at

Indian Institute of Remote Sensing. The guidance and support of colleagues in the making of

the thesis deserve a special mention. It is a pleasure to convey my gratitude to them all in my

humble acknowledgement.

The first of all I am extremely indebted to my supervisor, Dr. Sameer Saran, Scientist ‘SE’,

GID, IIRS. This work would not have been possible without his valuable guidance and

support. Under his guidance I successfully overcame many difficulties and learned a lot. He

has given me a freedom to explore and learn new things throughout the project. I gratefully

acknowledge his valuable suggestions not only bounded for my project work but also for my

better career prospective in the field of 3D geoinformation. His conviction will always

inspire me to work hard.

I am thankful to Dr. S.K. Srivastav, Head GID, Shri. P.L.N. Raju, Head RS &

Geoinformatics Group and Dr. Y. V. N. Krishna Murthy, Director IIRS for their valuable

advices, and support during my research work.

The fruitful discussion during project work with Amit Singh is highly acknowledged.

A sincere gratitude to Shri. Arun Kumar Sardar and staff for providing required help to

access library facilities, and to CMA members for maintaining computer systems including

software installation in GID Lab during the project work.

I pay respect to the Indian Institute of Remote Sensing, Dehradun for providing necessary

infrastructure and resources to learn and accomplish the M.Tech. Course in Remote Sensing

& GIS. My sincere thanks to Andhra University, Visakhapatnam for awarding me this

opportunity.

I extend my thanks to departments that have provided satellite and in-situ datasets for

research purpose. I would like to thank Data Equipment Section (DES) in IIRS for providing

archived satellite data used in this study. I would also like to thank Construction and

Maintenance Department (CMD) in IIRS for providing plan layout of study area.

I would like to express my heartfelt gratitude to Col. Sanjay Mohan for his valuable advice,

encouragement and moral support during the entire course. I would also like to thank Shri.

Manoj Semwal (Scientist, CSIR) for giving me a learning opportunity during course

curriculum.

My deep sense of gratitude towards my friend Unmesh Khati, for helping and encouraging

me willingly and selflessly during my research endeavour.

iv

I owe a special gratitude to my loving brother Harshad, my parents and relatives for

advising, educating and awarding me independence till now. I owe everything to them.

Besides this, I thank all colleagues who have knowingly and unknowingly helped me in the

successful completion of this project.

v

Certificate

This is to certify that Mr. Parag Sudhir Wate has carried out the dissertation entitled “3D

GIS Modeling at Semantic Level using CityGML for Urban Segment” in partial fulfilment

of the requirements for the award of M. Tech. in Remote Sensing and GIS. This work has

been carried out under the supervision of Dr. Sameer Saran, Scientist „SE‟, Geoinformatics

Department, Indian Institute of Remote Sensing, ISRO, Dehradun, Uttarakhand, India.

Dr. Sameer Saran Dr. S. K. Srivastav

Project Supervisor Head

Geoinformatics Department Geoinformatics Department

IIRS, Dehradun IIRS, Dehradun

Dr. S. K. Saha Dr. Y.V.N. Krishna Murthy

Dean (Academics) Director

IIRS, Dehradun IIRS, Dehradun

vi

Declaration

I, Parag Sudhir Wate, hereby declare that this dissertation entitled “3D GIS Modeling at

Semantic Level using CityGML for Urban Segment” submitted to Andhra University,

Visakhapatnam in partial fulfilment of the requirements for the award of M. Tech. in

Remote Sensing and GIS, is my own work and that to the best of my knowledge and belief.

It is a record of original research carried out by me under the guidance and supervision of

Dr. Sameer Saran, Scientist „SE‟, GID, Indian Institute of Remote Sensing, Dehradun. It

contains no material previously published or written by another person nor material which to

a substantial extent has been accepted for the award of any other degree or diploma of the

university or other institute of higher learning, except where due acknowledgment has been

made in the text.

Place: Dehradun Mr. Parag Sudhir Wate

Date:

vii

Dedicated to my Parents and brother Harshad

viii

Abstract

The study cogitate third dimensional modelling of geographic phenomenon related to urban

segment. The abstraction of multiresolution third dimensional (3D) geoinformation of city

objects in virtual model enables visualisation of physical structures in addition to dynamic

semantic and qualitative information. The recent studies in virtual 3D city modeling have

demonstrated its utility in different applications such as environmental simulations,

navigation, noise mapping, urban energy simulations, architecture and city planning. The

present research has evaluated the applicability of 3D city models in formulation of urban

energy conservation strategies. Due to urbanization, the exponential growth of cities in India

has resulted in increased use of non-renewable energy resources to meet the essential power

requirements of urban built environment. The urban planners are required to provide

innovative solutions in context of urban energy simulation based on virtual 3D City models.

Currently, 3D GIS data is acquired in discrete LODs (Level of Detail) depending upon the

application requirement and input data used. The present research work has proposed unique

3D data acquisition framework with reference to LoD concept. There are various 3D GIS

modeling softwares like Google SketchUp, ESRI CityEngine etc. which are being used for

geometry reconstruction lacking semantic information. City Geography Markup Language

(CityGML) is common semantic information model for spatio-semantic data storage and

exchange of virtual 3D city models. The 3D GIS data conversion from native format into

CityGML enhances it by providing spatio-semantic information in interoperable format. A

building information model of study area is generated using ESRI CityEngine and converted

into CityGML using developed customised converter. A model is also exported to energy

modelling program in gbXML schema. The spatio-semantic characteristics of derived

CityGML model have been explored for assessment of active and passive solar potential of

buildings. The effective areas of urban surfaces exposed to solar radiation are examined

using SunCast application in Virtual Environment software. The energy simulation results

are integrated with building semantic features to perform basic spatio-semantic analysis. An

approach is developed to extend CityGML classes with application specific properties using

Application Domain Extension (ADE) mechanism. The required input indicators and energy

simulation results are integrated into conceptual schema of CityGML Energy ADE

represented in Unified Modeling Language (UML). An instance document of ADE schema

could be used for interoperable data exchange between design and energy modelling

softwares. The presented research would help city planners and policy makers in formulation

of integrated planning and robust decision support system for urban energy infrastructure

with modifications in areas of modeling, simulation systems, and software platforms.

Keywords: LoD, CityGML, semantic analysis, urban energy, Energy ADE.

ix

Table of Contents

1 Introduction ........................................................................................................... 1

1.1 Background .............................................................................................................. 1

1.2 Motivation and Problem Statement .......................................................................... 2

1.2.1 Related to User Applications ............................................................................ 2

1.2.2 Related to Technology Progress ....................................................................... 2

1.3 Research Identification ............................................................................................. 3

1.3.1 Research Objective ........................................................................................... 3

1.3.1.1 Sub-objectives .............................................................................................. 3

1.3.2 Research Questions .......................................................................................... 4

1.3.3 Innovation aimed at .......................................................................................... 4

1.4 Research Hypothesis ................................................................................................ 4

1.5 Thesis Outline ........................................................................................................... 4

2 Materials and Methods ......................................................................................... 7

2.1 Study area and data ................................................................................................... 7

2.2 Tools and Instrumentation ........................................................................................ 8

2.2.1 Hardware Tools and Field Instrumentation ...................................................... 8

2.2.2 Software Tools .................................................................................................. 8

2.3 Field data analysis .................................................................................................... 8

2.3.1 Illustration for Calculation of Window-to-Wall Ratio from Field Data ......... 11

3 3D Data Acquisition Framework ....................................................................... 14

3.1 CityGML LoD Concept .......................................................................................... 14

3.1.1 Uniqueness of LoD Concept .......................................................................... 16

3.1.1.1 Properties of LoD concept .......................................................................... 16

3.2 Review of 3D Data Acquisition Techniques .......................................................... 16

3.2.1 Coarser LoD Reconstruction .......................................................................... 17

3.2.2 Detailed LoD Reconstruction ......................................................................... 18

3.3 Customised methodologies for LoD reconstruction ............................................... 19

3.3.1 LoD0 Methodology ........................................................................................ 19

3.3.2 LoD1 Methodology ........................................................................................ 21

3.3.3 LoD2 Methodology ........................................................................................ 21

3.3.4 LoD3 Methodology ........................................................................................ 23

x

3.3.5 LoD4 methodology ......................................................................................... 24

3.4 Matrix for 3D GIS Modeling Techniques .............................................................. 24

4 Transformation Mechanism for Interoperable Model ..................................... 26

4.1 Review of 3D data standards and formats .............................................................. 26

4.1.1 3D computer graphics and visualisation formats............................................ 26

4.1.2 Building Information Models (BIM) .............................................................. 27

4.1.3 Comparison of 3D standards and formats ...................................................... 27

4.2 Rationale for 3D City Modeling at Semantic Level ............................................... 28

4.2.1 CityGML (Version 2.0.0) ............................................................................... 28

4.2.1.1 CityGML LoD2 Building model ................................................................ 29

4.2.1.2 CityGML LoD3 Building model ................................................................ 30

4.3 Transformation Methodology ................................................................................. 31

4.3.1 LoD2 and LoD3 Building Model Generation ................................................. 31

4.3.2 Semantic Categorization ................................................................................. 32

4.3.3 Transformation of Google SketchUp model to CityGML .............................. 33

4.3.4 Storage of semantic components in Open source RDBMS ............................ 37

4.3.5 Model Preparation for export to Energy modelling program ......................... 37

4.3.6 Integration of Energy simulation data and semantic analysis ........................ 37

4.4 Dissemination of Interoperable Model ................................................................... 38

5 Semantic Analysis for Urban Energy conservation strategies ........................ 41

5.1 Review of Urban Energy conservation Case Studies ............................................. 41

5.2 Review of Solar Radiation Simulation algorithm in urban context ........................ 42

5.2.1 Basic Terminologies in Solar Radiation Model ............................................. 43

5.2.2 Sun-Earth Geometry ....................................................................................... 43

5.2.3 Beam Radiation on tilted surfaces .................................................................. 45

5.2.4 Diffused Radiation on tilted surfaces ............................................................. 45

5.2.4.1 Isotropic Diffuse Model ............................................................................. 46

5.2.5 Reflected Radiation from ground on tilted surfaces ....................................... 46

5.3 Semantic Analysis based on CityGML model ....................................................... 46

5.3.1 Solar Radiation Mapping on LoD2 model ..................................................... 46

5.3.2 Solar Luminance and Illuminance Mapping on LoD3 model ........................ 47

5.3.3 Basic semantic Query on LoD2 model ........................................................... 48

xi

6 Formulation of CityGML Energy ADE ............................................................. 52

6.1 Interoperability gap between BIM and Energy Models ......................................... 52

6.2 Overview of CityGML ADE mechanism ............................................................... 53

6.3 UML based modelling of CityGML ADE .............................................................. 54

6.4 Review of CityGML Energy ADE Test beds ......................................................... 55

6.4.1 Energy Atlas of Berlin – CityGML Energy ADE Test Bed ........................... 55

6.4.2 I-SCOPE Project – Solar ADE Test Bed ........................................................ 56

6.4.3 EnergyADE Proposal of the HFT Stuttgart and TU Munich ......................... 56

6.5 Conceptual schema for Proposed Energy ADE ...................................................... 57

7 Conclusions and Recommendations ................................................................... 59

7.1 Conclusion .............................................................................................................. 59

7.1.1 Answers to research questions ........................................................................ 59

7.2 Recommendations .................................................................................................. 60

7.3 Practical Application of Study ................................................................................ 61

References ................................................................................................................. 62

APPENDIX 1 ............................................................................................................ 66

xii

List of Abbreviations

3D GIS - Third Dimension Geographic Information System

ADE - Application Domain Extension

BIM - Building Information Modeling

CityGML - City Geography Markup Language

COLLADA - COLLAborative Design Activity

GML - Geography Markup Language

IESVE - Integrated Environmental Solutions Virtual Environment

IFC - Industry Foundation Classes

ISO - International Organisation for Standardisation

KML - Keyhole Markup Language

LoD - Level of Detail

OGC - Open Geospatial Consortium

UML - Unified Modeling Language

VRML - Virtual Reality Modelling Language

X3D - Extensible 3D

xiii

List of Figures

Figure 1-1: Proposed Research Methodology .......................................................................... 5

Figure 2-1: Geographic Location and PAN Imagery of Study area - IIRS Campus ................ 7

Figure 2-2: Ground Trace of control points ........................................................................... 10

Figure 2-3: GID building height measurement....................................................................... 12

Figure 2-4: GID window width measurement ........................................................................ 13

Figure3-1: Five discrete CityGML LoDs(Wate et al., 2013) ................................................. 15

Figure 3-2: Classification of acquisition techniques .............................................................. 17

Figure 3-3: LoD0 Methodology ............................................................................................. 19

Figure 3-4: Space use map of IIRS Campus .......................................................................... 20

Figure 3-5: LoD0 (2.5D) representation ................................................................................. 20


Figure 3-7: LoD1 representation ............................................................................................ 21


Figure 3-9: LoD2 representation ............................................................................................ 22

Figure 3-10: LoD3 Methodology ........................................................................................... 23

Figure 3-11: LoD3 representation .......................................................................................... 23

Figure 3-12: LoD4 Methodology ........................................................................................... 24

Figure 4-1: Illustration of CityGML LoD2 Building feature structure as UML instance

diagram ................................................................................................................................... 30

Figure 4-2: Illustration of CityGML LoD3 Building feature structure as UML instance

diagram ................................................................................................................................... 31

Figure 4-3: Transformation Methodology for derivation of Interoperable Model ................. 32

Figure 4-4: Thematically layered LoD2 Model ...................................................................... 33

Figure 4-5: Thematically layered LoD3 Model ...................................................................... 33

Figure 4-6: SketchUp to CityGML LoD2 customised converter using FME ........................ 35

Figure 4-7: SketchUp to CityGML LoD3 customised converter using FME ........................ 36

Figure 4-8: SketchUp to CityGML (LoD2 & LoD3) customised converter for Vegetation

object ...................................................................................................................................... 37

Figure 4-9: IIRS LoD2 CityGML model rendered in FZK Viewer ....................................... 39

Figure 4-10: IIRS LoD3 CityGML model rendered in FZK Viewer ..................................... 40

Figure 4-11: Basic semantic query results dissemination through Web browser ................... 40

Figure 5-1: Sun-Earth geometry(Duffie and Beckman, 1980) ............................................... 44

Figure 5-2: Beam radiation on tilted surface(Duffie and Beckman, 1980) ............................ 45

Figure 5-3: Components of diffuse radiation over sky dome(Perez et al., 1988) ................... 45

Figure 5-4: Mapping of annual solar radiation incident on building surfaces ........................ 47

Figure 5-5: Luminance Mapping and Glare analysis on LoD3 model ................................... 48

Figure 5-6: Illuminance mapping on external facade of LoD3 model ................................... 48

Figure 5-7: WallSurfaces above 50% threshold of annual percentage of exposed area ......... 49

Figure 5-8: WallSurfaces above 50% threshold of winter percentage of exposed area ......... 50

Figure 5-9: WallSurfaces above 50% threshold of summer percentage of exposed area ....... 50

Figure 6-1: Process workflow for information integration ..................................................... 52

xiv

Figure 6-2: Bidirectional workflow for information exchange and integration ..................... 53

Figure 6-3: Proposed Energy ADE schema ............................................................................ 58

Figure A1-1: LoD3 schema of Gymnasium Building Rule File............................................. 66

xv

List of Tables

Table 2-1: Hardware Details .................................................................................................... 8

Table 2-2: Details of Software used ......................................................................................... 8

Table 3-1: LoD Discretization(Albert et al., 2003) ................................................................ 16

Table 3-2: Matrix for 3D GIS Modelling Techniques ............................................................ 25

Table 4-1: 3D data standards and file formats(Zlatanova et al., 2012) .................................. 28

Table 4-2: LoD2 semantic features summary......................................................................... 39

Table 4-3: LoD3 semantic features summary......................................................................... 39

3D GIS Modeling at Semantic Level using CityGML for Urban Segment

1

1 Introduction

Twenty-first century is recognized as the century of globalization. The pace of globalization

has affected every human on Earth. Due to globalization, the small towns are getting

transformed into cities and small cities into the mega-cities. Mega-cities hold most of the

potential for economic growth (Zhang et al., 2011). The geographical location of a city plays

important role in its economic development. The geographical location of a particular mega-

city is unique and hence the amount of non-renewable and land resources available are

variable. The abundance or scarcity of such resources affects overall development of city. In

order to achieve appreciable economic growth and sustainable management of available land

resources, it is essential to develop underground and aboveground urban establishments

(Zhang et al., 2011).Therefore, it needs to be understood that the integration of latest

technological development in Architectural Engineering & Construction (AEC) along with

the third dimensional (3D) geospatial information is essential (Zhang et al., 2011). Thus, it is

found that the need for 3D City Models with geo-referenced information has increased

rapidly over last few years in the field of 3D urban planning & development,

telecommunication network planning, emergency services and disaster management

(Brugman, B., 2010).

1.1 Background

Commercial Off-The-Shelf (COTS) systems in 3D GIS market accounts for the efficient

handling of 3D data in 3D visualization aspect as well as for data processing to a certain

extent (Zlatanova et al., 2002). The abstraction of real world 3D objects into 3D data

structure and consequently in 3D spatial data model are the 3D GIS key research areas. The

current general purpose GIS vendors does not offer full 3D GIS functionality in terms of 3D

structuring (geometry and topology), 3D manipulation and 3D analysis (semantics) instead

they only offer tools for 3D navigation, animation, visualization and exploration (Zlatanova

et al., 2002).

Zlatanova et al., (2004) describes different 3D topological models such as Simplicial

complex, 3D Formal vector data structure (3DFDS), TEtrahedral Network (TEN), Simplified

Spatial Model (SSM), Urban Data Model (UDM), etc but they are still in hands of

researcher. The Constructive Solid Geometry (CSG), Tessellation (Voxel representation),

Tetrahedrons and Boundary representation are some 3D geometrical models that abstract and

represent real 3D objects (Richardson, 2002). The Boundary representation of 3D

geometrical model is the optimal model for representation of real world 3D objects like

present 2D objects because it is possible to map geometrical objects into topological

primitives and encode the same in spatial frameworks for spatial relationship representation

and detection (Richardson, 2002).


2

The major drawback of Boundary representation model is that the boundary representation

corresponding to a particular 3D object is not unique and constraints of lower order

primitives on higher order primitives may get very complex (Richardson, 2002). Moreover,

as far as the virtual 3D city models are concerned this model can only be used for

visualization purposes but not for thematic queries, spatio-semantic analysis tasks or spatial

data mining because complexity of tasks those have to be tackled and the information that

has to be processed will increase (Kolbe et al., 2012). The limited utility of geometrical

models restricts the broader use of 3D city models. Hence, there is need for more general

modeling approach for 3D city objects in terms of topological and spatio-semantic aspects to

account for the information requirements of various application fields (Kolbe et al., 2012).

1.2 Motivation and Problem Statement

1.2.1 Related to User Applications

GIS is a collection of information about the real world phenomenon abstracted into systems

to analyze the interesting patterns governing it and thus enabling to gain the knowledge of

surrounding world (Rolf A. de et al., 2001). In other words, GIS is termed as the integration

of semantic and geometric data along with the spatial relationships to ensure large scope for

analysis and also to serve many applications (Zlatanova et al., 2002). The 2D GIS analysis

has limitations in certain 3D urban segment situations. The situations such as Infrastructure

development to address the issue of interaction between newly designed city elements and

existing infrastructure; real estate market to address the issue of property tax collection

categorised according to floor-wise space use; prediction of increase in noise levels due to

new communication and transportation networks (Zlatanova, 2000), visualisation of design

for site planning of cultural and architectural monuments; modelling of water inundation

scenarios in urban areas (Breunig and Zlatanova, 2011) are distinct which requires unique

modeling and analysis approach. Thus, the need for 3D geo-referenced information for

modeling and analysis is extremely high.

1.2.2 Related to Technology Progress

The current GIS software-tools have also made significant contribution towards 3D GIS

development by improving 3D visualisation and animation (Zlatanova et al., 2002). The

other motivation behind 3D GIS development is hardware development in terms of increased

processing speeds, availability of large storage memory and disk space devices, especially

graphic processing units (GPUs) which aid3D data rendering(Richardson, 2002). There are

also positive developments in 3D GIS data acquisition techniques in context of each Level-

of-Detail (LoD) (Richardson, 2002). However, the current status of 3D operations which are

required to access and manipulate 3D spatial data are still in amateur state, non-standardised,

non-transparent, non user-friendly and requires large number of specialised settings


3

(Zlatanova et al., 2002). The conceptual models provide application dependent feature-

geometry linkage which is not conducive from interoperability point of view.

Moreover, the spatial operators operate only on x, y values and also models provide limited

semantic hierarchy (Zlatanova et al., 2002). The fact of lacking true 3D GIS functionality

such as efficient querying 3D geo-objects, 3D analyses (3D overlay, 3D buffering, 3D

shortest route), 3D inter-visibility, etc has been regularly reported in literature. In recent

years, CityGML has provided a semantic information model to abstract the virtual 3D city

models which can be shared over various vibrant user applications. Henceforth, the modeling

and analysis of 3D space at semantic level including aboveground and underground, indoor

and outdoor environment is needed to provide an optimum total solution (Zhang et al.,

2011).

1.3 Research Identification

In last few years, it was noted in literature that there had been no commonly agreed standard

available to represent and to exchange 3D city models in an interoperable way. Usually, 3D

city models were represented and exchanged in non GIS computer graphics standards (3D

Studio Max or VRML) lacking interoperability that were necessary for many user specific

applications of 3D city models for data exchange. But, from last couple of years onwards,

CityGML has emerged as a common geometrical and semantic model which enables and

facilitates many user specific applications of 3D city models. Currently, CityGML is widely

accepted OGC standard for 3D city models by geospatial industry (Gröger and Plümer,

2012). Hence, the need for advanced 3D data acquisitions for cities and encoding of same in

CityGML for semantic analysis is identified.

1.3.1 Research Objective

The primary objective of proposed research work is to encode the complex and geo-

referenced 3D vector data in CityGML at semantic level according to the user requirements.

1.3.1.1 Sub-objectives

1. To formulate a framework for 3D GIS data acquisition techniques in context of

CityGML LoD concept.

2. To derive a mechanism for transformation of 3D vector data referring to LoD2 and

LoD3 into CityGML semantic model.

3. To perform CityGML model based spatio-semantic analysis related to urban energy

conservation strategies.

4. To develop a conceptual schema for integration of semantic analysis results with

CityGML to allow interoperable exchange of application specific information

between heterogeneous systems.


4

1.3.2 Research Questions

1. How to abstract and represent the 3D vector data into a semantic model of CityGML

standard?

2. What kind of spatio-semantic analysis possible based on geometric and semantic

properties derived from CityGML model for urban energy conservation strategies?

3. How to formulate the conceptual schema for integration of application specific

information with CityGML model to allow interoperable and lossless exchange?

1.3.3 Innovation aimed at

The progress in 3D GIS till now have shown that there is still lack of true 3D model which

will allow user community to integrate heterogeneous representations and models from

different domains. The proposed 3D geo-objects modelling and analysis using CityGML will

focus on spatio-semantic integration of unique identity of these 3D entities along with

application specific information in an interoperable way.

1.4 Research Hypothesis

The previous studies in 3D geoinformation modelling and analysis in urban segment have

stated that there is lack of availability of interoperable 3D city model to address particular

problem. An interoperable 3D city dataset of urban block could be useful for data exchange

between urban design and simulation software platforms. The presented research would

attempt to establish end-to-end process workflow beginning with 3D data acquisition,

derivation of interoperable 3D city dataset of urban block, analysis and application

integration. Figure 1-1 shows the proposed methodology to implement process workflow for

3D city dataset based spatio-semantic analysis. The outcomes of the research work could be

formulation of data acquisition framework with reference to LoD, data transformation

mechanism for derivation of interoperable model and 3D city dataset based simulation for

user requirement specific application.

1.5 Thesis Outline

The proposed research work is outlined as follows: Chapter 1 gives an overview of research

topic, including introduction, topic background, motivation behind the investigation,

research objectives and questions, and organization of the thesis. Chapter 2 deals with the

description of geographic area under study, remote sensing and ancillary datasets, tools and

instrumentation required, and analysis of acquired field data. Chapter 3 describes the

CityGML LoD concept, different 3D data acquisition techniques and their implementation in

context of LoD. Chapter 4 discusses various native 3D dataset standards, rationale behind

semantic 3D city modelling and transformation methodology for interoperable 3D data

standard.


5

Figure 1-1: Proposed Research Methodology

Chapter 5 details urban energy conservation case studies and semantic analysis performed

using CityGML model. Chapter 6 provides the conceptual schema for integration of

application specific information with CityGML model using concept of Application Domain

Extension (ADE). Chapter 7 provides answers to the research questions, future scope and

practical applications of research work.

The thesis chapters are based on the following research papers and abstract which are

referred in text.

I. Wate P., Saran S., Srivastav S.K., Murthy Y.V.N.K., 2013. Formulation of

Hierarchical Framework for 3D-GIS Data Acquisition Techniques in context of

Level-of-Detail (LoD), in: 2013 IEEE Second International Conference on Image

Information Processing (ICIIP-2013). IEEE, Shimla, India, pp. 154–159.

doi:10.1109/ICIIP.2013.6707573.

II. Saran S., Wate P., Srivastav S.K., Murthy Y.V.N.K., 2014. CityGML at Semantic

Level for Urban Energy Conservation Strategies, in: Annals of GIS. Manuscript ID:

TAGI-2014-0023. (Under Revision).

III. Wate P., Saran S., 2014. Design of CityGML Energy Application Domain Extension

for Integration of Urban Solar Potential Indicators using object-oriented UML,

(Under Preparation).


6

In paper I, the authors have proposed 3D data acquisition framework in context of LoD

(Objective 1) which is referred as Chapter 3 of thesis. The paper II is divided into two

Chapter4 & Chapter 5 addressing data conversion and analysis process (Objective 2 &

Objective3). In paper III, the conceptual schema for integration of urban energy conservation

data with CityGML will be proposed (Objective 4).


7

2 Materials and Methods

2.1 Study area and data

The study was carried out on Indian Institute of Remote Sensing (IIRS) Campus, Dehradun,

India spread over an area of 84425.00519 sq m as shown inFigure 2-1. The study area is

geographically located between 78°02‟35.56” to 78°02‟52.224” East longitudes and

30°20‟23.0784” to 30°20‟29.8104” North latitudes. The average altitudinal height of

underlying terrain is 653.573 m above ellipsoidal datum (WGS 84) and lies at foot hills of

Himalayas.

Figure 2-1: Geographic Location and PAN Imagery of Study area - IIRS Campus

The IKONOS PAN and Cartosat 2 imagery of study area, and Cartosat 1 stereo pair of

Dehradun region archived by Data Equipment Section (DES) in IIRS have been used to

extract building footprints and map the open spaces. The Plan Layout of IIRS has been

obtained from Construction and Maintenance Department (CMD) in IIRS to generate Digital

Elevation Model (DEM). The data about the building dimensions has been obtained from

field survey.


8

2.2 Tools and Instrumentation

2.2.1 Hardware Tools and Field Instrumentation

The hardware tools and field instrumentation used in research has been listed in Table 2-1.

Table 2-1: Hardware Details

Hardware / Instrument Purpose

Trimble GNSS R7 To collect Ground Control Points (GCPs)

Leica Total Station TPS1200 To measure building dimensions

NIKON Digital Camera To texture 3D objects

2.2.2 Software Tools

The list of software used in research work has been provided in Table 2-2.

Table 2-2: Details of Software used

Software / Tool Purpose

ESRI ArcGIS 10 To perform georeferencing and map composition

Leica Photogrammetry Suite To generate terrain model & ortho-rectified images

PhotoModeler Scanner 6 Close-Range Photogrammetry software

ESRI CityEngine 2012 To create rule based 3D vector data

Google SketchUp 8 To perform pre-processing tasks

FME Desktop 2013 To execute data manipulation operations

FZK Viewer 4.1 To parse and render XML files

PostGIS 2.0 To store 3D vector data

gModeller SketchUp Plug-in To import and export gbXML files

IES Virtual Environment 2013 Simulation Platform

Enterprise Architect 9 To create UML diagrams

ShapeChange 2.0.0 To generate GML schemas (XSD files)

LiquidXml 2014 To edit and validate XML files

WampServer 2.2 To setup local server for hosting of application

2.3 Field data analysis

The differential GPS survey has been carried out to collect the two near accurate Ground

Control Points (GCPs): one near the direction board and second one in front of the IIRS

Main building. These GCPs were used to orient the total station instrument with plane metric

and height tolerance limits of 10 cm and 50 cm respectively. The total station points within

tolerance limits are considered while some exceptionally erroneous readings were excluded

from further analysis. Out of these valid total station points (200 points approx.) collected,

the sparsely distributed points were used for geo-referencing of satellite imagery. The


9

location for point collection has been chosen in such way that they are accessible for total

station instrument setup and are well distributed in all directions (360° view) of building. The

map of collected total station points is shown in Figure 2-2.


10

Figure 2-2: Ground Trace of control points

Direction Board

Main Building


11

For every building in study area, these surrounding total station points were used to measure

building dimensions such as building height, storey height, etc. in addition to door and

window dimension measurements. The point cloud dataset of surveyed points were analyzed

in ArcGIS LiDAR Analyst to derive the values for building height, storey height, window

height, window width, door width and door height. The database of derived values for every

building in campus was created for its further use in 3D object generation.

2.3.1 Illustration for Calculation of Window-to-Wall Ratio from Field Data

Figure 2-3 & Figure 2-4 show building height and window width measurement illustration

using ArcGIS LiDAR Analyst for Geoinformatics department (GID) building in IIRS

campus. In Figure 2-3, the highlighted points showed using LiDAR Analyst profile view

represent bottom and top corner points of GID building. The table view shown provides

plane metric coordinates and height information of these corner points. The building height

is obtained using measurement tool and it is measured to be as 7.124 meters. In Figure 2-4,

the highlighted points showed are the four corner points of building window. These top,

bottom, right and left corner points are used to measure the window dimensions. For

particular window of building wall (gml_id = "fme_bf881a27-7857-4529-a21e-

c25d0347fc9d"), the width and height measured are 0.75 and 1.5 meters respectively. This

wall is having six windows of mentioned dimensions. Therefore, total openings area =

6*1.5*0.75 = 6.75 sq. m. and computed wall area = 54.83 sq. m. So, Window-to-Wall Ratio

= 6.75/54.83 = 12.31%.In general, depending upon building dimensions the Window-to-

Wall ratio lies between 10-20%. The window-wall ratio of 20% is considered for further

computations of incident solar radiation on bounding surfaces.


12

Figure 2-3: GID building height measurement


13

Figure 2-4: GID window width measurement


14

3 3D Data Acquisition Framework

3.1 CityGML LoD Concept

Like multi-scale representation concept of topographic maps, the 3D objects which are

nearer to synoptic view are detailed represented than farther objects(Gröger and Plümer,

2012). LoD concept in 3D City Modeling depicts the respective data acquisition method with

particular application requirement. Biljecki F., et al., (2013) reviews LoD concept in

different standards and products such as City Geography Markup Language (CityGML)

standard, Blom3D product range and NAVTEQ datasets based on semantic properties,

surface texture, geometry and type of city objects. The author has restructured LoD concept

stated in these standards into respective Sub-levels of details (SLoD) based three major

driving components: exterior and interior geometry, and external appearance. The author has

also proposed a framework specification for LoD definition in context of geometry quality,

appearance, positional accuracy, geometry details, granularity of semantic hierarchy and

quality of semantic details.

The new building LoD concept proposed by Benner et al., (2013) is based on geometric and

semantic properties of building exterior and interior shell in five distinct levels. The authors

discuss deficits of current CityGML LoD concept in applicability of one LoD concept to all

thematic modules and also its flexibility in hybrid LoD approach realization. The authors

have also mentioned about lack of metadata about LoD information in current concept.

There are three classes of LoD which have been described in computer graphics domain:

namely discrete LoD, continuous LoD and view-dependent LoD (Luebke et al., 2003). The

discrete LoD categorization for building thematic module is discussed in CityGML multi-

scale modelling approach. This approach allows simultaneous multi-scale resolution

representation of same object in a single CityGML dataset.

Biljecki, (2013) has proposed for integration of scale as spatial dimension in 3D city

modelling and also researched applicability of hyper-dimensional theories to represent object

in 4D space (x, y, z and scale). The outcomes of the proposed investigation will be to

formulate definition for context-aware LoD, intermediate scale (hybrid LoD) and mixed-

scale or perspective view oriented LoD.

CityGML encoding standard for 3D city modeling issued by Open Geospatial Consortium

(OGC) and ISO TC211 describes multi-scale modeling of real world 3D objects specifically

urban objects in five discrete LoD(Kolbe et al., 2012). The conceptual differentiation

between CityGML discrete LoD mentioned in OGC project document can be reviewed in

following manner:


15

LoD0 is earth‟s terrain feature abstraction as a Digital Terrain Model (DTM) with

draping of ortho-rectified satellite images over terrain. An urban object such as

building may be represented in LoD0 as footprint and/or roof-edge boundary

polygon extracted from ortho images. LoD0 is a coarsest level representation of 3D

city models. It is a model representation at regional and landscape scale.

LoD1 is abstraction of objects over earth‟s terrain as a Digital Surface Model

(DSM). A building object in LoD1 may be represented into prismatic blocks with

vertical walls and horizontal flat roof structures. LoD1 is a block level representation

of 3D city objects at city scale.

LoD2 is representation of specific type of city objects such as residential buildings

and landmarks differentiated by thematically textured boundary surfaces and

prototypic roof structures at city district scale.

LoD3 is representation of architectural features of outermost building structure

along with textured building surfaces, installation parts, facades and openings. LoD3

is a finest resolution model for exterior shell with textured wall and detailed roof

structures including openings such as doors and windows. It is a representation at

neighbourhood scale.

LoD4 is abstraction of architectural features of innermost building shell with interior

structures along with LoD3 representation of outermost shell. CityGML LoD4 is

finest resolution model for building interior consisting of rooms, stairs, furniture,

interior doors and windows.

An example of a building object represented in CityGML LoD concept is shown in

Figure3-1. A referred building object is a Gymnasium Building in IIRS Campus.

Figure3-1: Five discrete CityGML LoDs(Wate et al., 2013)


16

3.1.1 Uniqueness of LoD Concept

Table 3-1shows five discrete LoD specifying the distinct features that can be identified in

each LoD. The continuous LoD is used in Computer Graphics (CG) domain which aims only

at continuous visualization (Gröger and Plümer, 2012). The discrete LoD not only addresses

the geometric aspects of 3D entities but also semantic ones and that too with increase in

semantic richness with increase in LoD.

3.1.1.1 Properties of LoD concept

Facilitates the integration of features represented in same LoD in an interoperable

way.

Each LoD implies requirements for certain class of applications.

Allows multi-resolution representation of a particular feature.

The peculiar property of LoD concept is that particular LoD intuitively depicts the

respective data acquisition method.

Table 3-1: LoD Discretization (Albert et al., 2003)

LoD Abstract Feature

Representation

Distinct Features Identified Accuracy

(position / height)

LoD0 Digital Terrain Model (DTM) Building footprints lowest

LoD1 Digital Surface Model (DSM) Building as prismatic object 5/5m

LoD2 Building Structures with

prototypic features

Buildings with prototypic roof

shapes

2/2m

LoD3 Buildings with architectural

features

Buildings with openings and

textured wall surface

0.5/0.5m

LoD4 Buildings with interior

architectural features

Buildings rooms with interior

furniture and texturing

0.2/0.2m

3.2 Review of 3D Data Acquisition Techniques

The recent developments in satellite and airborne sensor technology providing improved

image qualities especially in terms of spatial resolution has helped the 3D GIS progress in

terms of 3D reconstruction of cities for various user specific applications. In order to

reconstruct the 3D representation of earth surface objects, the techniques such as Satellite

Photogrammetry, LiDAR data processing in addition to building structure extraction

algorithms are applied on satellite observation based data (Sirmacek et al., 2012). Moreover,

the field based observation techniques such as Close-Range Photogrammetry; Total station

survey; Terrestrial Laser scanning also contributes towards 3D reconstruction of real world

objects. In order to generate 3D city datasets with reference to LoD, various data acquisition

techniques have been investigated. These 3D data acquisition techniques are widely

classified as satellite and field based observation techniques, and Volunteered Geographic

Information (VGI) based techniques as shown inFigure 3-2.


17

Figure 3-2: Classification of acquisition techniques

3.2.1 Coarser LoD Reconstruction

Satellite based observation techniques are usually deployed for coarser CityGML LoD

reconstruction specifically LoD0 and LoD1.

Satellite Photogrammetry is a technique which uses either a rigorous or Rational Function

Model (RFM) of satellite sensors to reconstruct the physical imaging geometry of

topographic features on earth surface from stereo-pair images by transforming the image

space coordinates to object space coordinates (DeWitt and Wolf, 2000). The conditions of

co-linearity and co-planarity are satisfied for reconstruction of earth‟s terrain topography.

The obtained digital terrain model (DTM) is 2.5D representation with elevation as attribute

value. It is a coarsest LoD (LoD0) reconstruction technique of terrain features.

LiDAR data processing involves generation of digital surface models (DSM) of natural and

artificial objects on earth‟s surface from geo-referenced LIDAR point cloud data obtained

from space borne or airborne laser scanning (Kalantaitė et al., 2012).There are techniques to

classify the point cloud data as earth‟s surface features and natural or artificial objects.

Bartels et al., (2006) have demonstrated the methodology for classification of LiDAR point

cloud data (DSM) using skewness balancing algorithm with overall accuracy of 95.65% and

kappa coefficient of 91.27%. The algorithm is based on assumption that the object points

disturb the normal distribution of raw point cloud data and by filtering object points from

raw data the ground points are obtained. The algorithm iterates over raw data under

boundary conditions of statistical measures of distribution i.e. statistical moments: skewness

(sk) & kurtosis (ku). With sk> 0 and ku> 3, raw data represents the dominance of peaks and

algorithm goes on classifying maximum value as object point until sk = 0 and ku = 3. The

algorithm results in extraction of ground points (DTM = DSM - nDSM) and object points

(nDSM) as LoD0 and LoD1 representation respectively.

Arefi et al., (2008) have proposed hybrid approach to extract building outlines from LiDAR

DSM for LoD definition based 3D building models generation. The method provides two


18

LoD: LoD0 and LoD1. LoD0 (building outlines and DTM) is obtained by filtering of non-

ground points from LiDAR DSM using geodesic reconstruction process. The extracted

building outlines are segmented and approximated to a straight line using MBR & RANSAC

fitting and merging algorithms. The building height is estimated by averaging of cloud points

lying inside the building outline to generate the prismatic model (LoD1). The other similar

approach involves segmentation of DSMs, building edge extraction, masking, Hough

transformation, line segmentation and polygon tracing to extract approximate building

structures (Oda et al.).

3.2.2 Detailed LoD Reconstruction

The field based data acquisition techniques are used for reconstruction of relatively finer

resolution 3D city models (LoD2 & LoD3).

Terrestrial Laser Scanning (TLS) acquires 3D point cloud data that represents detailed 3D

features of building object. Tang et al., (2008) have illustrated surface reconstruction and

texturing over 3D TIN model of building obtained from pre-processing and matching of

adjacent LiDAR point cloud datasets. Nguyen et al., (2012) have also demonstrated principle

for 3D object reconstruction by processing of TLS acquired point cloud data in RiSCAN

PRO 3D modelling software module. The principle governs registration of scan positions,

merging and matching of adjacent scans, 3D mesh generation and texturing.

Close-range photogrammetry is a technique which uses photographic measurements for 3D

reconstruction of imaged object. The terrestrial images are acquired from all directions at

total station surveyed points surrounding the object to ensure sufficient overlap between two

adjacent images. The principle of epipolar geometry (space resection method) detects the

points in space corresponding to a particular control point in different photographs. The

control points on different photographs are used to define extrinsic (orientation & position)

and intrinsic camera (focal length and distortion) parameters. Brunetaud et al., (2012) have

demonstrated close-range photogrammetry method to reconstruct the east tower of the castle

of Chambord, France based on calibrated photographs and their projections as textures on

generated 3D model.

Goetz and Zipf, (2012) have developed framework for automatic derivation of 3D CityGML

models (LoD1 & LoD2) from Volunteered Geographic Information (VGI). The proposed

framework has stated two-tier method for derivation of CityGML models from

OpenStreetMap (OSM). In first step, the set of rules for semantic mapping between key-

value pair in OSM with CityGML attributes has been defined and secondly, the geometry

reconstruction techniques for CityGML LoD have been devised. The similar approach has

been adopted and continued for creation of detailed 3D CityGML models (LoD3 & LoD4)

from OSM (Goetz, 2013).


19

3.3 Customised methodologies for LoD reconstruction

The customized methodologies based on mentioned techniques have been developed by

(Wate et al., 2013)in order to formulate 3D GIS data acquisition framework with reference to

LoD.

3.3.1 LoD0 Methodology

Figure 3-3 depicts the data acquisition methodology for LoD0 model. In this methodology,

Leica Photogrammetry Suite software was used to generate DTM and orthodata (building

footprints) from satellite stereo pair and GCPs. Then, the subsequent map composition of

extracted building footprints and open spaces was performed in ArcMap application of

ArcGIS. LoD0 is not a “true” 3D city model as it is boundary representation of features in

2D with height as an attribute. LoD0 usually include ortho-rectified data of building features

and plane metric information of other land use-land cover features. Figure 3-4shows space

use map of IIRS campus. LoD0 (Figure 3-5) reconstruction of IIRS campus was

complemented with DEM obtained from surveyed plan Layout contour layer.

Figure 3-3: LoD0 Methodology


20

Figure 3-4: Space use map of IIRS Campus

Figure 3-5: LoD0 (2.5D) representation


21


Figure 3-6 shows the data acquisition methodology for LoD1 model. In this methodology,

extracted building footprints were extruded with Z height value by enabling extrusion

property of feature in ArcScene application of ArcGIS. The height value for individual

buildings was obtained from total station survey. Figure 3-7 depicts LoD1 representation

with imagery draped over DEM.


Figure 3-7: LoD1 representation


Figure 3-8 represents the data acquisition methodology for LoD2 model. This methodology

uses close-range photogrammetry approach to obtain textured 3D models by generating tie

points in adjacent photographs. This approach of LoD2 reconstruction was implemented


22

only for Gymnasium building in campus (Figure3-1). Since, methodology involves extensive

manual intervention to generate tie points and wire mesh model of object, it is not feasible to

adopt this approach for object reconstruction at urban design scale. During the course of

research work, this approach was modified and automatic 3D object reconstruction approach

was developed. The developed approach involves the rule-based 3D geometry creation in

ESRI CityEngine software based on Computer Generated Architecture (CGA) grammar. The

method requires only prototype rule to be written which is efficient and flexible to

reconstruct 3D geometry at urban design scale. Figure 3-9 shows the LoD2 representation of

staff quarters building with gable roof type.




23


Figure 3-10shows the data acquisition methodology for LoD3 model. This methodology uses

hand drafted content design tool called Google SketchUp to reconstruct architectural features

of buildings. The manual 3D content creation is a tedious and extensive process for urban

design problem. For current research work, the CGA rule was created for reconstruction of

detailed architectural features of certain institutional and residential buildings in study area.

The approach is semi-automatic method as rule file is unique for particular building and

needs minor modifications in CGA grammar for other type of building. The sample rule file

schema for LoD2 reconstruction is given in Appendix (Figure A1-1).Figure 3-11 shows

Main building in study area with detailed architectural features. The sample CGA grammar

file for LoD3 reconstruction is given in Appendix.




24

3.3.5 LoD4 methodology

Figure 3-12 shows the data acquisition methodology for LoD4 model. This methodology

makes use of interior components available in Google SketchUp in addition to interior

texturing to realise Indoor 3D virtualization. The approach was adopted to demonstrate

LoD4 reconstruction of only Gymnasium building in study area (Figure3-1). The LoD4

reconstruction of other buildings in study area lies outside the scope of objectives of present

investigation.


3.4 Matrix for 3D GIS Modeling Techniques

There are certain demerits and limitations of each 3D GIS data acquisition methodology. The

2.5D model generated with stated technique does not account for the accurate positioning of

building footprints with respect to the underlying terrain. But, plane metric error can be

minimized by obtaining GCPs through DGPS survey. The proposed methodology for

prismatic 3D model does not address the errors in building height but they were quantified

from total station survey readings. The stated technique used for LoD2 model (close-range

photogrammetry) approximates the 3D surface orientation thus results in violation of

orthogonal representation of 3D structures. This discrepancy was eliminated and

reconstruction process was automated by using rule-based modeling in CityEngine. The

methodology used for finest 3D model generation is extensively manual process and hence

the abstraction of every detail was cumbersome and also complex from geometry

representation point of view. It was semi-automated by using CGA grammar based modeling

in CityEngine. Indoor model acquisition methodology is also a manual process and raises

questions about the level of indoor modeling acceptable from administration point of view.


25

The proposed hierarchical framework suggests suitable 3D data acquisition mechanism in

context of respective LoD. There are certain drawbacks of proposed methodologies which

need to be understood while deriving a model for specific user application. Table 3-2shows

3D GIS modeling matrix which provides general guidelines for 3D datasets generation in

context of distinct CityGML LoD.

Table 3-2: Matrix for 3D GIS Modelling Techniques

LoD Aerial Acquisition Terrestrial Acquisition

Satellite

Photogrammetry

Spaceborne /

Airborne LiDAR

Close-Range

Photogrammetry

Mobile Laser

Scanner

LoD0

LoD1

LoD2

LoD3

LoD4

The current developments in 3D GIS account only for data acquisition, visualization and

animation. Moreover, the available 3D operationwhich are required to access and manipulate

3D spatial data are very limited and does not address application specific spatial analysis.The

3D GIS models derived from proposed framework can be encoded into a common semantic

information model according to user requirements to provide true 3D GIS functionality in an

interoperable way.


26

4 Transformation Mechanism for Interoperable Model

4.1 Review of 3D data standards and formats

The standards and formats for representation, storage and exchange of 3D city models are

the results of application requirements or purpose of use. The focus of today‟s 3D city

models presentation is at geovisualisation using computer graphics method and their

representation and design using Building Information Models (BIM)(Gröger and Plümer,

2012). The most demanding are the data storage and exchange standards and formats like

CityGML, Industry Foundation Classes (IFC) (ISO, 2005b) and green building XML

(gbXML) for lossless and interoperable information integration between different Computer-

aided architectural design (CAAD) and geospatial analysis softwares. The bi-directional

information exchange is advantageous as geospatial models are benefited from detailed BIM

representation and geospatial analysis can provide spatial reference and enrich BIM with

application specific information.

Although the 3D standards are emerged for fulfilling goals of various international standard

organizations in different domains, the information content in them deals mainly with object

geometry, surface textures, semantics and their inter-relationships (Zlatanova et al., 2012).

The authors have distinguished surface based (e.g. Constructive Solid Geometry-CSG,

Boundary Representation-BRep) and volume based (e.g. Voxel) representations for

modelling of 3D objects. CSG representation is used by IFC data exchange format as its

main emphasis is on construction and design of buildings using basic construction elements

(slabs, beams, walls, etc.), while CityGML spatial data model represents 3D geometry by

boundary representation to describe human conceptualisation of buildings (Gröger and

Plümer, 2012).

4.1.1 3D computer graphics and visualisation formats

The 3D city models abstracted in X3D (ISO, 2005a),3ds Max, 3D PDF, VRML (ISO, 2004)

formats belong computer graphics domain (Gröger and Plümer, 2012). Virtual Reality

Markup Language (VRML) is a web standard for modelling of interactive 3D realistic scenes

(RHYNE, 1999). Later, VRML consortium (Feb-98) had approved Geo-VRML format to

generate and exchange georeferenced data in VRML. Web consortium (1998) had developed

XML based file format which was improvement of VRML but too complex for

interpretation. 3D PDF format exchanges the 3D design information in normal PDF file

format which can be integrated with text document using Adobe software(Zlatanova et al.,

2012).

Keyhole Markup Language (KML) and Collaborative Design Activity (COLLADA) are

geovisualisation standards for geometry representation without semantics. KML is an XML


27

based format for geovisualisation of objects in Google Earth and for web applications. Since,

its adoption by Open Geospatial Consortium (OGC), the support for geometry is given by

Geographic Markup Language (GML). COLLADA describes 3D content with geometry,

topology, and texture but without semantics. All the mentioned graphic and geovisualisation

formats only support realistic and interactive visualisation.

4.1.2 Building Information Models (BIM)

BIM is the semantic information modelling of building exterior and interior structures with

their physical and functional characteristics (Kolbe, 2009 & Gröger and Plümer, 2012). IFC,

an ISO standard developed by International Alliance for Interoperability (IAI) is used as data

exchange format for building information with support of WGS84 global reference system in

its current version 2x Edition 3 (buildingSMART, 2007). IFC provides semantic model only

for building objects with single LoD representation unlike CityGML semantic model.

However, relation between two semantic models (IFC and CityGML) for BIM (design

model) and geospatial models (real world model) has been researched to develop common

unified spatial applications with minimum conversion overhead. El-Mekawy et al., (2012)

has proposed unified building model (UBM) by analyzing relation between CityGML

features and corresponding IFC feature with 1:1, 1:n and n:1 relationship. Isikdag and

Zlatanova, (2009) have proposed framework for automatic derivation of buildings in

different CityGML LoDs from BIM by defining set of rules for semantic mapping and

geometry transformation.

4.1.3 Comparison of 3D standards and formats

Zlatanova et al., (2012) have compared standards based on 11 criterias categorised into three

groups: shape and appearance, object identification and attributes, and syntax and web

visualisation. The criterias of geometry, topology, texture and LoD are classified into shape

and appearance group. The criterias of objects, semantics and attributes are categorised in

object identification and attribute group, while syntax and web visualisation group deals with

the XML, web, georeferencing and vendor acceptance criterias. The authors provide

overview of discussed 3D data standards and its excerpt is listed in Table 4-1. The standards

(DXF, VRML, X3D and COLLADA) from Computer Aided Design (CAD) domain in

general follow geometry and texture criterias of 1st category. On other hand, the standards

(KML, Geo-VRML, 3D PDF) from Computer Graphics (CG) domain are in accordance with

the geo-referencing and XML based web geo-visualization category. CityGML and IFC 3D

standard from Building Information Model (BIM) accounts for the object identity

(semantics), attributes along with its thematic properties. CityGML is a GML3 based object-

oriented model for real 3D objects representation in terms of their semantic, geometry,

topology and appearance characteristics (Kolbe et al., 2012).


28

Table 4-1: 3D data standards and file formats (Zlatanova et al., 2012)

Category Criteria Domain 3D standards

and file formats

Shape and

Appearance

Geometry, texture,

LoD, topology

Computer Aided Design

(CAD)

DXF, VRML,

X3D, COLLADA

Syntax and Web

visualization

XML and Web based,

georeferencing

Computer Graphics (CG) KML, Geo-

VRML, 3D PDF

Object identification

and attributes

Object semantics and

attributes

Building Information Model

(BIM)

CityGML, IFC

4.2 Rationale for 3D City Modeling at Semantic Level

In last decade, it was reportedly noted in literature that there had been no commonly agreed

standard available to represent and exchange 3D city models in an interoperable way. Hence,

the need for more general interoperable modeling approach for 3D city objects in terms of

topological and semantic aspects to account for the information requirements of various

vibrant user application fields was identified (Richardson, 2002). Usually, 3D city models

were represented and exchanged in non GIS computer graphics standards (3D Studio Max or

VRML) lacking interoperability as it was necessary for many user specific applications of

3D city models (Gröger and Plümer, 2012).

But, from last couple of years onwards, CityGML has emerged as a common geometric and

semantic information model to abstract the virtual 3D city objects and to facilitate lossless

data exchange in interoperable way. The only geometry representation of 3D objects may

results in more complex and redundant 3D content. This restricts utility of geometrical

models for broader use of 3D city models (Kolbe et al., 2012). CityGML is widely accepted

OGC standard for 3D city models by geospatial industry to facilitate spatio-semantic

analysis (Gröger and Plümer, 2012). Henceforth, the modeling and encoding of 3D space in

CityGML standard at semantic level including aboveground and underground, indoor and

outdoor built environment is needed to provide an optimum total solution for integrated

urban planning and decision making.

4.2.1 CityGML (Version 2.0.0)

Semantic information about 3D city object (e.g. building) is human conceptualisation of

different geometries (e.g. Wall surface, Roof surface, Door, Window) associated with it.

Semantic information helps to reduce ambiguities for geometric integration, if it is

coherently structured with respect to geometry (Stadler and Kolbe, 2007). “CityGML is a

common semantic information model based on ISO 191xx family standards for the

representation of 3D urban objects that can be shared over different applications” (Kolbe et

al., 2012). CityGML is an open data model and XML-based format for representation of 3D

geometries of most relevant city objects such as buildings, city furniture, land use and


29

vegetation (Krüger A. and Kolbe T.H., 2012). The applicability of model for different

applications facilitates the cost-effective sustainable maintenance of various 3D city models.

The class diagram representation of relevant topographic objects in cities abstracts their

appearance, geometrical, topological and semantic properties along with application specific

data (e.g. values concerning urban energy consumption and production) to develop specific

Application Domain Extension (ADE) for strategic urban energy planning. CityGML

standard compliant 3D city model facilitates large and small scale representation of urban 3D

objects in different levels of detail simultaneously. The peculiarity of CityGML is

interoperability, in general and lossless information exchange between heterogeneous GI

systems and users, in particular (Kolbe et al., 2012).

The 3D urban segment can be decomposed into several natural and man-made entities. City

is a man-made entity which abstracts major components such as buildings, commutation

networks, city furniture, etc in a structured manner in terms of their semantics and well

defined spatial and thematic properties. Building component is the most important man-

made object in a city. Building module in CityGML contributes to a significant quantum of

semantics of City module. It also accounts for multi-resolution representation of buildings

and their installation parts. A particular module in CityGML is specified by Unified

Modeling Language (UML) instance diagrams. The geometry of building semantics is given

by externally referenced GML3.1.1 application schema. In order to support data integration

and spatio-semantic queries, it is essential to have structurally consistent models in terms of

their geometry and semantic properties (Stadler and Kolbe, 2007).

4.2.1.1 CityGML LoD2 Building model

Figure 4-1 illustrates CityGML feature structure as UML instance diagram of basic LoD2

building model. CityGML Building model is defined by the Building thematic extension

module. It allows multi-resolution representation of thematic and spatial properties of

buildings and building parts in discrete LoD.

Figure 4-1 shows corresponding classification of BoundarySurfaces. The _AbstractBuilding

is parent class of building model which is sub-class of thematic class _Site and which is sub-

class of root class _CityObject. Building and BuildingPart are the specialized classes of

_AbstractBuilding class. GML3 feature properties and CityGML specific properties from

_CityObject class are inherited by _AbstractBuilding class. The building class, building

function (e.g. residential, commercial, etc.), building usage, year of construction and

demolition, roof type, building and storey height attributes of _AbstractBuilding class are

inherited by Building and BuildingPart.

In LoD2 building model, the exterior surface of building is semantically categorised into

bounding _BoundarySurface class. The _BoundarySurface class primarily consists of


30

semantic features like wall (WallSurface), roof (RoofSurface) and ground plate

(GroundSurface). The surface geometry of each feature represents its spatial properties.

Figure 4-1: Illustration of CityGML LoD2 Building feature structure as UML instance diagram

4.2.1.2 CityGML LoD3 Building model

Figure 4-2 illustrates CityGML feature structure as UML instance diagram of basic LoD3

building model. The outer shell of building in LoD3 is semantically distinguished with

bounding _BoundarySurface and BuildingInstallation thematic classes. A _BoundarySurface

is further decomposed mainly into thematic features like wall (WallSurface), roof

(RoofSurface) and ground plate (GroundSurface). The BuildingInstallation class accounts

for building elements such as balconies, chimneys, dormers, etc. related mostly to outer

appearance of buildings. The openings in _BoundarySurface objects are represented by

doors and windows thematic objects.

s1: gml: Solid

b: Building

function = educational

bp1: BuildingPart

roofType = flat

rs1: RoofSurface

su1: gml: Surface

ws1: WallSurface

su2: gml: Surface

consistsOfBuildingPart

lod2Solid boundedBy

boundedBy

lod2MultiSurface

lod2MultiSurface


31

Figure 4-2: Illustration of CityGML LoD3 Building feature structure as UML instance diagram

4.3 Transformation Methodology

In order to generate 3D model, the IKONOS imagery of IIRS campus was rectified and

georeferenced with reference to Ground control points (GCPs) obtained from field survey.

Map composition of IIRS campus area was carried out and building footprints were

obtained. The extracted building footprints were imported as shapefile in ESRI CityEngine

software. Figure 4-3shows methodology for derivation of interoperable model.

4.3.1 LoD2 and LoD3 Building Model Generation

The LoD2 model of IIRS campus and LoD3 model of institutional buildings were generated

from building footprint shapefile using CGA grammar in ESRI CityEngine software. The

extensive field data about the building & openings dimensions was being used for

reconstruction of actual architectural geometry. The 3D shape models of building were

exported to KML format for pre-processing. The trees surrounding the buildings modelled in

CityEngine was also exported in KML format.

s1: gml: Solid

b: Building

function = educational

bp1: BuildingPart

roofType = flat

bi: BuildingInstallation

su4: gml: Surface

rs1: RoofSurface

su1: gml: Surface

ws1: WallSurface

su2: gml: Surface

d: Door

w: Window

su4: gml: Surface

su3: gml: Surface

consistsOfBuildingPart

lod1Solid

boundedBy

boundedBy

lod3MultiSurface

opening

outerBuildingInstallation

lod3Geometry lod3MultiSurface

lod3MultiSurface


32

Figure 4-3: Transformation Methodology for derivation of Interoperable Model

4.3.2 Semantic Categorization

The LoD2 and LoD3 KML models were imported in Google SketchUp for thematic layering

of models into different semantic components of building. The LoD2 model was

thematically layered into bounding features such as WallSurface, RoofSurface,

GroundSurface and Tree (Figure 4-4) while LoD3 model was layered into


33

BuildingInstallation, WallSurface, RoofSurface, GroundSurface, Door, Window and Tree

(Figure 4-5).

Figure 4-4: Thematically layered LoD2 Model

Figure 4-5: Thematically layered LoD3 Model

4.3.3 Transformation of Google SketchUp model to CityGML

Thematic model (LoD2 & LoD3) prepared in Google SketchUp was transformed into

CityGML using Feature Manipulation Engine (FME) by Safe Software. FME Desktop 2013

supports over 300 formats and 400 transformers to allow interoperability between different

data formats including point cloud, 3D, raster, database, vector and XML formats. FME

Desktop 2013 is useful for data conversion, transformation, integration and validation. It also

supports database solutions including PostGIS, Oracle and Microsoft SQL Server.A Google

SketchUp to CityGML converter with SketchUp Reader and CityGML writer was developed

using sequence of built-in FME transformers for LoD2, LoD3 building model and also for


34

Vegetation (Tree) model. For LoD3 model, the feature classes for Building, RoofSurface,

WallSurface, GroundSurface, BuildingInstallation, Window and Door have been created

with Building as parent feature class. While for LoD2 model, feature classes only for

bounding surfaces (Building, RoofSurface, WallSurface and GroundSurface) have been

created. The FME workspace which contains readers, writers and transformers of LoD2 and

LoD3 converter is shown in Figure 4-6 &

Figure 4-7 respectively. A separate converter was developed for vegetation object to create

SolitaryVegetationObject feature class (Figure 4-8). The execution of conversion process

was iterated for every LoD2, LoD3 building and all vegetation objects.


35

Figure 4-6: SketchUp to CityGML LoD2 customised converter using FME


36

Figure 4-7: SketchUp to CityGML LoD3 customised converter using FME


37

LoD3 Converter: GeometryPropertyExtractor transformer in customised converter extracts

the geometry trait or name from different SketchUp layers by trait “sketchup_layer_name” –

Building, BuildingInstallation, RoofSurface, WallSurface, GroundSurface, Window and

Door. The CityGMLGeometrySetter transformer was used to assign CityGML LoD name

and Feature Role to CityGML Feature corresponding to extracted geometry trait.

GeometryValidator transformer repairs geometry defects in processed input features.

AttributeCreator creates new CityGML features and assigns unique ids to generated features.

The CityGML feature writers in FME imports the XSD schema of core and base profiles to

write CityGML instance document. The citygml_parent_id for the RoofSurface, WallSurface

and GroundSurface was set to Building ID. While for Building, the citygml_parent_id was

set to CityModel ID. Thus, the corresponding CityGML LoD3 model has been derived from

SketchUp model.

Figure 4-8: SketchUp to CityGML (LoD2 & LoD3) customised converter for Vegetation object

4.3.4 Storage of semantic components in Open source RDBMS

The separate CityGML files generated were combined into a single CityGML file for

respective LoD. The combined CityGML model corresponding to particular LoD was

imported in open source PostGIS database for semantic querying. There were nine relations

(Building, BuildingInstallation, CityModel, RoofSurface, WallSurface, GroundSurface,

Door, Window, SolitaryVegetationObject) of LoD3 model and six relations (Building,

CityModel, RoofSurface, WallSurface, GroundSurface, SolitaryVegetationObject) of LoD2

model imported into separate PostGIS database using FME CityGML reader and PostGIS

writer with key of “citygml_parent_id” field.

4.3.5 Model Preparation for export to Energy modelling program

The building information designed in Google Sketchup is transformed into space model by

selecting shading surfaces as bounding surfaces and creating the Room space using

gModeller-Energy Analysis Plug-in for Google Sketchup. The space model is then exported

to IESVE energy modelling program in gbXML schema to allow interoperable use of

building information model into energy analysis software program.

4.3.6 Integration of Energy simulation data and semantic analysis

For energy conservation strategy in urban segment, building object was considered as

hemispherical dome and sun elevation above horizon was categorized as 30°, 60°, 90°, 120°

and 150°. The solar energy potential of WallSurface and RoofSurface of LoD2 building

model was assessed by quantising amount of solar intensity irradiated on walls and roofs of

building using SunCast application in IES Virtual Environment Software. The application

provides effective percentage of wall surface areas exposed to insolation during every hour


38

throughout the year. The unique identity of semantic components of model is stored as

distinct relation in PostGIS database. The integration of application specific data of

percentage of sun exposed walls in winter and summer was performed with respective

semantic relations using SQL.

A basic semantic query was performed to find out number of semantic components receiving

solar radiation for maximum duration of year and to observe the effect of seasonal variation

(summer and winter) on number of bounding elements receiving solar radiation for

maximum duration of season.

4.4 Dissemination of Interoperable Model

The LoD2 and LoD3 CityGML XML schema obtained from converter uses externally

referenced GML3.1.1 schema for storage of geometry of semantic components. It derives

required XML elements of building module from the XSD document of externally

referenced base profile module. It derives gml: envelope element from CityGML core

module to define the spatial bounding box of building model. The cityObjectMember

element of core module contains all elements required for definition of building semantic

components.

A building object is bounded by WallSurface with openings which are stored as exterior and

interior LinearRing geometry type. It stores the coordinates of points forming the LinearRing

geometry in gml: posList element. The building installation semantic component is stored in

bldg: BuildingInstallation element as lod3Geometry with gml: MultiSurface, gml:

surfaceMember, gml: Polygon, gml: LinearRing as child elements and bldg:

outerBuildingInstallation as parent element. On similar lines building surface bounding

components such as WallSurface, RoofSurface and GroundSurface are stored as

lod2MultiSurface and lod3MultiSurfacegeometries for respective LoD2 and LoD3 models.

The opening semantic components (Door and Window) of building are also stored as

lod3MultiSurface geometries with hierarchy of gml: MultiSurface, gml: surfaceMember,

gml: Polygon, gml: LinearRing and gml: posList as child elements inside bldg: opening

parent element.

Table 4-2 & Table 4-3 show the summary of semantic features of all buildings (Lod2 &

Lod3) in study area written by the FME CityGML writer of developed customised converter.

Figure 4-9 & Figure 4-10 shows the LoD2 & LoD3 CityGML building model with semantic

layers rendered in FZK viewer.


39

Table 4-2: LoD2 semantic features summary

Features of IIRS LoD2 model Number of Features

CityModel 001

Building 034

WallSurface 410

RoofSurface 034

GroundSurface 034

SolitaryVegetationObject 020

Total Features Written 533

Table 4-3: LoD3 semantic features summary

Features of IIRS LoD3 model Number of Features

CityModel 0001

Building 0011

BuildingInstallation 0011

WallSurface 1359

RoofSurface 0014

GroundSurface 0011

Window 0642

Door 0061

SolitaryVegetationObject 0009

Total Features Written 2119

Figure 4-9: IIRS LoD2 CityGML model rendered in FZK Viewer


40

Figure 4-10: IIRS LoD3 CityGML model rendered in FZK Viewer

Figure 4-11shows basic semantic analysis results in web browser for LoD3 model of

Geoinformatics department building in 3D PDF format. It provides information about

insolation on semantic components of building for given time of day. The web interface

allows choosing of good, medium and poor intensity facades at every hour of day. It also

facilitates end user to analyse the particular openings of buildings receiving enough

illumination during day time.

Figure 4-11: Basic semantic query results dissemination through Web browser


41

5 Semantic Analysis for Urban Energy conservation strategies

5.1 Review of Urban Energy conservation Case Studies

The exponential growth of cities in India due to urbanization resulted in increased use of

non-renewable energy resources to meet the essential power requirements of urban built

environment. The urban segment consumes large percentage of total energy produced. The

energy produced by burning fossil fuels leads to emission of greenhouse gases affecting the

environment and posing adverse climate change impacts. Hence, meeting of increase urban

energy demand by reducing carbon footprint is a challenge for cities to make necessary

amendments in their urban energy infrastructure strategies towards efficient and sustainable

management of available energy resources.The urban energy modeling studies are facing the

problems of data availability of real-time city-wide energy consumption which is useful for

simulation of various urban energy scenarios and subsequent decision making. Hence, the

complex urban energy system in sustainable cities must model, simulate and analyze the

spatially distributed urban energy demand and supply based on building energy component

characteristics and urban morphology (2D & 3D) at building and city scale respectively.

The European Institute for Energy Research (EIFER) has proposed 3D urban morphology

based spatial model for simulation of heat energy demand and spatial Agent-Based models

for simulation of smart grids (Bahu et al., 2013). The authors have used 3D city models for

assessment of solar potential and heat energy demand of residential buildings. In another

study, Krüger A. and Kolbe T.H., (2012) have derived energy-related key indicators of

buildings from spatio-semantic properties of CityGML compliant 3D city models for

estimation of heating energy consumption in buildings. The authors have also mentioned

about applicability of proposed research into analysis of different energy simulation

scenarios for urban energy planning and decision making. The integration of 3D geodata and

urban energy models is advantageous for both urban and energy planning domains. The

energy models are enriched with high resolution spatio-semantic content from 3D city

models while on other hand 3D models are complemented with description of their energy

performance and use at semantic level.

Ratti et al., (2005) have attempted to integrate the urban geoinformation and energy system

by assessing the impacts of urban geometry, building design, system efficiency and occupant

behaviour on building energy performance. The values for urban geometry (e.g. surface-to-

volume ratio, passive zones identification and zones) were calculated from DEM and

Lighting and Thermal (LT) model. The LT model developed by Baker et al., (2000) provides

the relation between architectural parameters and building energy consumption as a function

of illuminance level, boiler efficiency and heat transfer coefficients (U-values). Ratti et al.,

(2005) have also studied the effects of urban morphology on building energy consumption


42

by defining urban parameters (passive / non-passive zones, orientation of facade, urban

horizon angle, and obstruction sky view) in LT model.

Carrión et al., (2010) have proposed a methodology for estimation of energetic rehabilitation

state of buildings by calculating heating energy consumption as a function of building energy

characteristics derived from CityGML compliant 3D city model. The estimation is based on

assumption that heating energy consumption of buildings is strongly correlated to their

energy relevant geometric and semantic parameters. The building energy parameter values

for volume, assignable area, surface-to-volume (s-v) ratio, average storey height and

building type were derived from spatio-semantic properties of CityGML 3D city model.

Eicker et al., (2012) have also developed methodology for estimation of heating energy

demand of residential buildings from virtual 3D city models.

Strzalka et al., (2010) have demonstrated implementation of two simulation models in

Integrated Simulation Environment Language (INSEL) software for estimation of heating

energy demand at city scale and building level. The heating energy demand was calculated

using parameters of heated volume, s-v ratio, and heat transfer coefficients and weather data.

The first model implemented degree day method(Sarak and Satman, 2003) considering only

transmission losses through building envelope while second model implemented energy

balance method taking into account transmission and ventilation losses, and solar and

internal heat gains. Kaden and Kolbe, (2013) have also calculated net energy demand for

space heating based on energy balance method as function of building geometry, building

usage, building construction material parameters derived from semantic 3D city models.

Compared to these methods, current research work aims to assess the active and passive

solar potential of built environment at urban design scale. The study aims to examine urban

surfaces exposed to global (direct and diffuse) solar irradiance for identification of suitable

location to collect solar energy on seasonal basis. The qualitative computation of amount of

irradiance on roofs and facades in terms of exposed areas is performed for active solar

potential assessment. The amount solar illuminance through the facade fenestrations is

determined to develop approach for passive solar potential assessment. The values of floor

area and percentage exposed areas of facades and roofs from simulation model are integrated

using SQL with the respective semantic components of developed CityGML LoD2 and

LoD3 model stored in RDBMS. The semantic analysis is performed and simulation results

are visualised in viewer for sustainable urban planning and efficient decision making.

5.2 Review of Solar Radiation Simulation algorithm in urban context

The study of solar radiation over urban texture by Lee and Zlatanova, (2009) have been

identified as the most relevant to the current research. The authors have presented the

environmental analysis of 2.5D urban surface model extracted from LIDAR and image

processing techniques to examine the energy performance at city scale. The authors have


43

also implemented the tool to estimate percentage of urban surfaces exposed to direct solar

radiation and amount of total solar irradiance (diffuse and beam) incident on facades and

roofs.

5.2.1 Basic Terminologies in Solar Radiation Model

Beam Radiation: “The solar radiation received from sun without having been scattered by

the atmosphere”(Duffie and Beckman, 1980). It is also often called as direct normal

radiation. It is the amount of solar radiation incident on a surface perpendicular to beam of

radiation (Ibeam).

Diffuse Radiation: “The solar radiation received from sun after direction has been changed

by scattering by atmosphere” (Idiff). It has two components: diffuse radiation from sky (Isdiff)

and diffuse radiation from ground (Igdiff).

Total Solar Radiation: “The sum of beam and diffuse solar radiation on a surface”. It is also

referred as global radiation on surface (Iglob).

Irradiance: “The rate at which radiant energy is incident on a per unit area of a surface

perpendicular to the direction of propagation of radiation, at mean earth-sun distance,

outside of atmosphere”. It is given by solar constant Gsc equal to 1367 W/m2adopted by

World Radiation Center (WRC) with uncertainty of 1%.

Irradiation or Radiant Exposure (J/m2): “The incident energy per unit area on a surface”.

The term insolation is used for solar energy irradiation.

5.2.2 Sun-Earth Geometry

The geometry between any oriented surface relative to earth‟s surface at any time and beam

radiation or position of sun relative to surface is defined by sun altitude (s), zenith (z) and

azimuth angle (s) and angle of beam radiation incidence (), surface azimuth angle () and

slope (), location (latitude-angular location , longitude) of surface on earth and hour angle

(). A geometrical relationship was described in terms of these angles by Benrod and Bock,

(1934). Figure 5-1 shows the sun-earth geometry between a beam radiation and tilted

surface.


44

Figure 5-1: Sun-Earth geometry(Duffie and Beckman, 1980)

The relation between the beam angle of incidence and surface angles is given by equations

mentioned in Duffie and Beckman, (1980).

cos 𝜃 = sin 𝛿 sin 𝜑 cos 𝛽 − sin 𝛿 cos 𝜑 sin 𝛽 cos 𝛾+ cos 𝛿 cos 𝜑 cos 𝛽 cos 𝜔+ cos 𝛿 sin 𝜑 sin 𝛽 cos 𝛾 cos 𝜔+ cos 𝛿 sin 𝛽 sin 𝛾 sin 𝜔

(5-1)

cos 𝜃 = cos 𝜃𝑧 cos 𝛽 + sin 𝜃𝑧 sin 𝛽 cos (𝛾𝑠 − 𝛾) (5-2)

For vertical surfaces (e.g. facades), = 90°, substituting in (5-1,

cos 𝜃 = − sin 𝛿 cos 𝜑 cos 𝛾 + cos 𝛿 sin 𝜑 cos 𝛾 cos 𝜔+ cos 𝛿 sin 𝛾 sin 𝜔

(5-3)

For horizontal surfaces (e.g. roofs), = 0° then = z, substituting in (5-1,

cos 𝜃𝑧 = cos 𝜑 cos 𝛿 cos 𝜔 + sin 𝜑 sin 𝛿 (5-4)

In order to estimate active and passive solar potential of urban surfaces, it is required to

calculate solar radiation incident on facades and windows ( = 90°), flat and gabled roof

structures, and horizontal surfaces. The total incident solar radiation on tilted surface is the

sum of beam radiation, three components of diffuse radiation from sky (mainly, isotropic

component in this research context) and radiation reflected from ground.


45

5.2.3 Beam Radiation on tilted surfaces

For solar calculation purposes, it is required to calculate the radiation on tilted surface (in

context of this research work, facades and gabled roof structures) from measurements or

simulation estimates of radiation on horizontal surfaces. Usually, measurement data of total

radiation on horizontal surface is available and, beam, diffuse and total radiation data on

tilted surface is derived. The relation between beam radiation on tilted surface to that on

horizontal surface is given by the geometric factor; Rb. Figure 5-2illustrates the ray diagram

for calculation of radiation on horizontal and tilted surface.

𝑅𝑏 = 𝐵𝑒𝑎𝑚 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑛 𝑡𝑖𝑙𝑡𝑒𝑑 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 (𝐼𝑡𝑏𝑒𝑎𝑚 )

𝐵𝑒𝑎𝑚 𝑟𝑎𝑑𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑛 𝑕𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 (𝐼𝑕𝑏𝑒𝑎𝑚 )=

𝐼𝑏𝑒𝑎𝑚 cos 𝜃

𝐼𝑏𝑒𝑎𝑚 cos 𝜃𝑧

= 𝑐𝑜𝑠 𝜃

𝑐𝑜𝑠 𝜃𝑧

(5-5)

The beam radiation on tilted surface is given by (5-6.

𝐼𝑡𝑏𝑒𝑎𝑚 = 𝑅𝑏𝐼𝑕𝑏𝑒𝑎𝑚 (5-6)

Figure 5-2: Beam radiation on tilted surface(Duffie and Beckman, 1980)

5.2.4 Diffused Radiation on tilted surfaces

The study of profiles of diffuse radiation from sky by Coulson, (1975) has categorised

diffuse radiation into three main components – isotropic component received uniformly from

sky and two anisotropic component: circumsolar diffuse from forward scattering of solar

radiation concentrated in part of sky around the sun and horizon brightening concentrated

near horizon shown in Figure 5-3.

Figure 5-3: Components of diffuse radiation over sky dome(Perez et al., 1988)

Ibeam

Ibeam

Ihbeam

Itbeam


46

5.2.4.1 Isotropic Diffuse Model

The isotropic diffuse model derived by Liu and Jordan, (1963)deals only with the isotropic

component receiving diffuse radiation uniformly from sky. The diffuse radiation on tilted

surface at slope is given by the geometric factor of diffuse radiation on horizontal surface.

The diffuse geometric factor (Rd) is also referred as the view factor to the sky and given by

(1 + cos 𝛽)/2. Therefore, the diffuse radiation on tilted surface (Itiltedsdiff) due to isotropic sky

component is given by (5-7.

𝐼𝑡𝑖𝑙𝑡𝑒𝑑𝑠𝑑𝑖𝑓𝑓 = 𝐼𝑕𝑠𝑑𝑖𝑓𝑓 (1 + cos 𝛽) 2 (5-7)

In current research, IES Virtual Environment software was used for calculation of incident

solar radiation on created urban model. The simulation software calculates diffuse radiation

by considering only from isotropic component and no contribution from anisotropic

component. Therefore, total incident solar radiation assumes only isotropic diffuse

component.

5.2.5 Reflected Radiation from ground on tilted surfaces

The reflected radiation (Igdiff) from ground (diffuse reflector) on tilted surface is given by the

product of radiation incident on ground surface (I), view factor to the ground

[(1 − cos 𝛽) 2 ] and diffuse reflectance of ground (g). ( (5-8)

𝐼𝑔𝑑𝑖𝑓𝑓 = 𝐼 𝜌𝑔 (1 − cos 𝛽) 2 (5-8)

Therefore, the total solar radiation (Itotal) incident on tilted surface is given by

𝐼𝑡𝑜𝑡𝑎𝑙 = 𝐼𝑡𝑏𝑒𝑎𝑚 + 𝐼𝑡𝑖𝑙𝑡𝑒𝑑𝑠𝑑𝑖𝑓𝑓 + 𝐼𝑔𝑑𝑖𝑓𝑓

= 𝑅𝑏𝐼𝑕𝑏𝑒𝑎𝑚 + 𝐼𝑕𝑠𝑑𝑖𝑓𝑓 (1 + 𝑐𝑜𝑠 𝛽) 2 + 𝐼 𝜌𝑔 (1 − 𝑐𝑜𝑠 𝛽) 2

(5-9)

The study of renewable energy potential by Tereci et al., (2009) have evaluated two

applications for implementation of solar energy systems at city quarter scale. The active

solar potential of roof surfaces in terms of photovoltaic and thermal energy has been

calculated from values of total incident solar radiation, effective roof surface area, efficiency

factor of PV and thermal systems, and correction factors of their orientation.

5.3 Semantic Analysis based on CityGML model

5.3.1 Solar Radiation Mapping on LoD2 model

The SunCast application in IES Virtual Environment software calculates percentage of sun

exposed areas in year and also insolation on wall surfaces using sun-earth geometry defined


47

by solar angles (zenith, azimuth, and local angle of incidence), date, time, plane orientation

and tilt, plane surface azimuth, site longitude and latitude. The sun-earth geometry

establishes the relation between beam radiation and oriented plane on earth‟s surface. It

allows generating of temporal images which can be used for solar surface shading and

insolation analysis. The software has computed the total incident solar Irradiance values are

for full year using object location information and weather data files of point nearest to it.

The simulated full year global radiation values on walls and roofs can be used to quantify the

solar thermal and photovoltaic potential of building objects in study area. Figure 5-4 shows

mapping of incident solar radiation on vertical and horizontal surfaces of building objects.

Figure 5-4: Mapping of annual solar radiation incident on building surfaces

The application provides simulation with temporal granularity of hour, day and month which

is useful to evaluate building semantic features exposed to sun for longer duration during

summer and winter. The temporal simulation plays important role in calculation of actual

solar panel size and their placement on walls and roofs to easily collect incident solar energy.

The SunCast application can be used to analyze the effects of surrounding building and tree

obstructions on concerned buildings and also visualize different building orientations at

designing stage.

5.3.2 Solar Luminance and Illuminance Mapping on LoD3 model

The RadianceIES application in IES Virtual Environment software visualizes the distribution

of amount of light available in given 3D environment. It is useful to compute the solar

illuminance on semantic feature surfaces (windows), luminance due to facades, daylight


48

factors and perform glare analysis. RadianceIES works on “ray-tracing” algorithm which

traces ray of light from eye position into the source and generates luminance and illuminance

images based on loaded sky conditions. Figure 5-5shows luminance mapping and glare

analysis through openings of south-west oriented facade of Geoinformatics department

building in study area due to reflections surrounding building and tree obstructions at 15:00

hrs on April 10.

Figure 5-5: Luminance Mapping and Glare analysis on LoD3 model

Figure 5-6 shows illuminance on external south-west oriented building facade and openings

due to loaded sky conditions at 15:00 hrs on April 10. The illuminance mapping can be

performed on facades of all buildings in study area for analysis of daylight conditions and

glare due to reflections from surrounding obstructions.

Figure 5-6: Illuminance mapping on external facade of LoD3 model

5.3.3 Basic semantic Query on LoD2 model

The presented simulation could be useful to address certain queries related to semantic

features of building object. The values for percentage of exposed areas in summer, winter

and throughout the year of walls and roofs are integrated with respective tables WallSurface

and RoofSurface in PostGIS RDBMS. The results of basic semantic query performed were

visualised in viewer of FME data inspector.

The first basic query was performed to find out number of walls and roofs receiving solar

radiation for maximum duration of year. The criterion was set on percentage of wall or roof


49

area exposed to radiation throughout the year. A particular wall or roof is receiving solar

radiation for maximum duration, if its annual percentage of area exposed to radiation is

above the threshold. After evaluating the values of percentage areas, the threshold of 50%

was set for walls and 80% was set for roofs. There were 107 walls out of total 410 and 29

roofs out of total 34 retrieved above the set threshold. The wall with ID gml_id =

“GML_21fa3755-12ba-4cfe-9fa2-3891662f6102” of International hostel building was with

maximum percentage of exposed area of 80%, while wall with ID gml_id =

“GML_e465dc6c-550e-4058-af89-d8d4ebf480f0” of Lecture hall building was with

minimum of 50.34%. Figure 5-7 shows the walls of building above the threshold,

highlighting wall exposed to radiation for maximum duration of year.

Figure 5-7: WallSurfaces above 50% threshold of annual percentage of exposed area (The solar

radiation is available on 80% area of highlighted wall throughout the year, which is maximum)

The second query was to observe the effect of seasonal variation (summer and winter) on

number of bounding elements receiving solar radiation for maximum duration of season. The

same criterion of previous query was set for this query.

For winter season, there were 127 walls out of total 410 and 30 roofs out of total 34 retrieved

above the set threshold. The wall with ID gml_id = “GML_d1964098-5f94-4695-8432-

2199d4fc56e6” and gml_id = “GML_d1964098-5f94-4695-8432-2199d4fc56e6” of

International hostel building was with maximum percentage of exposed area of 100%, while

wall with ID gml_id = “GML_810d6b99-c893-42c1-98b3-8b7575789e2d” of Type C2

Quarters building was with minimum of 50.08%. Figure 5-8 shows the walls of building

above the threshold, highlighting wall exposed to radiation for maximum duration of winter

season.


50

Figure 5-8: WallSurfaces above 50% threshold of winter percentage of exposed area (The solar

radiation is available on whole area of highlighted wall throughout winter season, which is maximum)

For summer season, there were 83 walls out of total 410 and 30 roofs out of total 34

retrieved above the set threshold. The wall with ID gml_id = “GML_21fa3755-12ba-4cfe-

9fa2-3891662f6102” of International hostel building and ID gml_id = “GML_8c1db66c-

ae23-4d57-bcb8-196671644ef6” Type C2 Quarters building was with maximum percentage

of exposed area of 63.75%, while wall with ID gml_id = “GML_5980b93f-8e46-485c-b0a2-

f92fb8fe5b25” of Guest House Room building was with minimum of 50.39%.shows the

walls of building above the threshold, highlighting wall exposed to radiation for maximum

duration of summer season.

Figure 5-9: WallSurfaces above 50% threshold of summer percentage of exposed area (The solar

radiation is available on 63.75% area of highlighted wall throughout summer season, which is

maximum)


51

The identification of effective facade areas exposed to radiation provides the active solar

potential assessment at urban design scale by accumulating maximum incident solar energy.

Depending upon total incident solar radiation values and efficiency factors of solar panels,

the amount of photovoltaic and thermal energy potential can be quantified. For passive solar

assessment at urban design scale, the daylight penetrations through facade fenestrations can

be assessed. The calculations of amount of daylight penetrations and light efficacy of

electrical systems (lumens/watt) could give the savings in electricity used for lighting.


52

6 Formulation of CityGML Energy ADE

6.1 Interoperability gap between BIM and Energy Models

With the advent of energy rating systems (e.g. LEED – Leadership in Energy and

Environment Design) and energy efficiency requirements of built environment, the

assessment of energy performance of new buildings and refurbishment rates of existing

buildings has become important compliance in building design process. In order to

implement the energy efficient measures for building, it is essential that spatio-semantic

information interoperability between BIM softwares and energy modelling softwares must

exist. Interoperability facilitates the different domain teams (design, engineering and energy

modelling) to interact with single building model and to make necessary changes in design

depending upon the analysis of “what-if” scenarios. But still, the BIM-energy modelling

interoperability explored so far is unidirectional process work flow between BIM and energy

simulation systems.

The unidirectional process workflow was implemented using Green Building XML schema

(referred to as “gbXML”) to export the building information model into energy modelling

softwares. In order to benefit the BIM from energy performance results (workflow from

energy models to BIM), the process of integration of energy simulation results with semantic

features from CityGML based building model was attempted. The integrated CityGML

model was used as basis for formulation of conceptual schema for CityGML Energy

Application Domain Extension (ADE). Figure 6-1 elaborates the process workflow

attempted in present investigation.

Figure 6-1: Process workflow for information integration

The application domain extension (ADE) mechanism discussed in OGC document for

CityGML standard can simplify the above workflow by providing XML based exchange of


53

information with BIM and energy simulation softwares. Figure 6-2 depicts simplified

bidirectional workflow for integration and exchange of BIM and simulation information.

Figure 6-2: Bidirectional workflow for information exchange and integration

6.2 Overview of CityGML ADE mechanism

CityGML is interoperable information model which defines mostly all the relevant city

objects and their attributes to address broad range of applications. However, model allows

extending of its capabilities to include requirements of specific user application. CityGML

ADE concept provides mechanism for integration of simulation input parameters and results

with CityGML in form of conceptual schema to allow interoperable information exchange

between design and simulation systems. CityGML provides two mechanisms to define

application specific objects and their attributes for encoding of simulation indicators and

results which are not explicitly modelled in CityGML (Kolbe et al., 2012):

1. Generic objects and attributes: The generic extensions of CityGML are given by

GenericCityObject and _genericAttribute classes defined in Generics thematic

extension module. The attributes and features which are not modelled by predefined

by thematic CityGML classes are incorporated into any _CityObject as generic

objects without any change in CityGML XML schema. For example, current

CityGML version does not provide explicit thematic models for embankments and

city walls.

2. Application Domain Extension (ADE): ADE mechanism specifies additions of

application specific properties to particular CityGML module by defining an extra

XML schema definition file based on the CityGML schema definitions with its own

namespace, explicitly importing the XML schema definition of extended CityGML

module. An ADE schema can extend one or more CityGML module schemas. An

OGC document defining CityGML standard has specified Noise ADE for

Environmental Noise Dispersion simulation.

ADE mechanism allows validation of instance documents against both the extended

CityGML and ADE schema which is advantageous for different GI users working in same

application field to maintain semantic and syntactic interoperability. ADE mechanism

provides two ways extending existing CityGML modules or classes. The first way, is to

define new application specific feature types in ADE classes derived from respective abstract

or concrete CityGML classes allowing inheritance of all properties and associations. The

second way is to extend existing CityGML class with application specific properties by


54

“hook” mechanism. A “hook” in every XML schema definition of CityGML class or feature

facilitates inclusion of additional properties into it by ADEs using substitution of attributes

and properties. A “hook” element is defined by “xsd: anyType” data type from XSD

namespace and implemented as a GML property of the form

“_GenericApplicationPropertyOf<Featuretypename>”where <Featuretypename>is equal to

name of CityGML class or feature in which it is incorporated. The arbitrary number of

application specific properties can be defined for CityGML feature or class with XML

content with name “_GenericApplication-PropertyOf<Featuretypename>”. A “hook”

element is assigned to substitution group in concerned CityGML module namespace by

defining corresponding “_GenericApplicationPropertyOf<Featuretypename>”.

6.3 UML based modelling of CityGML ADE

Van den Brink et al., (2013) have given UML based modelling approach to develop

CityGML ADE. The authors have used model driven approach to extend CityGML data

model for inclusion of application specific information starting from UML diagrams. Out of

six alternatives described and compared, an optimal alternative was chosen for modelling of

ADEs in UML. These six alternatives were evaluated to address problem of representing

sub-classing and substitution of properties (“ade_hooks”) in UML. In order to overcome this

problem, an optimal alternative of adding these properties in a subclass in ADE package and

suppressing it from XML schema generation was selected. The subclasses have same name

as CityGML class they are extending with stereotype <<ADEElement>> and <<ADE>>

assigned to these subclasses and their specialized relation respectively. This representation

depicts that stereotypes marked subclasses are special subclasses adding only properties to

CityGML class and are exempted while creating XML schema. The authors have also

generated an XML schema (GML application schema)from IMGeo ADE defined in UML

(XMI file of ADE UML model exported from Enterprise Architect) using ShapeChange Java

tool which implements UML to GML encoding rules described in ISO standards. A custom

encoding rule was added in ShapeChange for classes with <<ADEElement>> stereotype in

order to suppress them from GML application schema generation but at the same time

allowing addition of properties to ADE namespace as substitutes for the CityGML

“_GenericApplicationPropertyOf<Featuretypename>” hooks. As there are no guidelines

provided in current OGC CityGML specification for extension of CityGML to correctly

model an ADE in UML, the described model driven approach will be considered as best

practice for modelling of CityGML Energy ADE in UML, which is the goal of current

investigation.


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6.4 Review of CityGML Energy ADE Test beds

6.4.1 Energy Atlas of Berlin – CityGML Energy ADE Test Bed

Urban energy consumption in terms of power/electricity, water, gas and heating is directly

correlated with building information such as envelope volume, assignable area, its type and

no. of inhabitants (Krüger A. and Kolbe T.H., 2012). The case study of area in Berlin

demonstrates the existence of relationship between energy consumption values and building

spatio-semantic properties derived from CityGML model of Berlin City. The test bed

provides estimation of heating energy consumption based on building information

parameters, indexes, elementary and complex indicators. Indicator values are derived from

the semantic, geometric and topological properties of CityGML compliant 3D city model of

Berlin. The excerpt from study (Krüger A. and Kolbe T.H., 2012) is mentioned below.

Elementary indicator values explicitly taken from semantic properties of building are number

of storeys, building usage, construction year and no. of accommodation units.

𝐸𝐼𝑁𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑆𝑡𝑜𝑟𝑖𝑒𝑠 = 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑛𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑆𝑡𝑜𝑟𝑒𝑦𝑠

𝐸𝐼𝑈𝑠𝑎𝑔𝑒 = 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛

𝐸𝐼𝐶𝑜𝑛𝑠𝑡𝑟 𝑢𝑐𝑡𝑖𝑜𝑛𝑌𝑒𝑎𝑟 = 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑦𝑒𝑎𝑟𝑂𝑓𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛

𝐸𝐼𝐴𝑐𝑐𝑜𝑚𝑜𝑑𝑎𝑡𝑖𝑜𝑛𝑈𝑛𝑖𝑡𝑠 = 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑛𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝐴𝑐𝑐𝑜𝑚𝑜𝑑𝑎𝑡𝑖𝑜𝑛𝑈𝑛𝑖𝑡𝑠

Complex indicators such as building height and heated volume are extracted from overall

geometry of building, while building vicinity indicator is derived from topological property

of buildings in city.

𝐶𝐼𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔𝐻𝑒𝑖𝑔 𝑕𝑡 = 𝑓(𝐺𝑒𝑜𝑚𝑒𝑡𝑟𝑦[𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔])

𝐶𝐼𝐻𝑒𝑎𝑡𝑒𝑑𝑉𝑜𝑙𝑢𝑚𝑒 = 𝑓(𝐺𝑒𝑜𝑚𝑒𝑡𝑟𝑦[𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔])

𝐶𝐼𝑉𝑖𝑐𝑖𝑛𝑖𝑡𝑦 = 𝑓(𝑇𝑜𝑝𝑜𝑙𝑜𝑔𝑦[𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔])

There are some indicators which are derived from previously defined elementary and

complex indicators such as storey height, floor area and building type. Those are defined as:

𝐶𝐼𝑆𝑡𝑜𝑟𝑒𝑦𝐻𝑒𝑖𝑔 𝑕𝑡 = 𝑓(𝐶𝐼𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔𝐻𝑒𝑖𝑔 𝑕𝑡 , 𝐸𝐼𝑁𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑆𝑡𝑜𝑟𝑖𝑒𝑠 )

𝐶𝐼𝐴𝑠𝑠𝑖𝑔𝑛𝑎𝑏𝑙𝑒𝐴𝑟 𝑒𝑎 = 𝑓(𝐶𝐼𝑆𝑡𝑜𝑟𝑒𝑦𝐻𝑒𝑖𝑔 𝑕𝑡 , 𝐶𝐼𝐻𝑒𝑎𝑡𝑒𝑑𝑉𝑜𝑙𝑢𝑚𝑒 )

𝐶𝐼𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔𝑇𝑦𝑝𝑒 = 𝑓(𝐸𝐼𝑈𝑠𝑎𝑔𝑒 , 𝐸𝐼𝑁𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑆𝑡𝑜𝑟𝑖𝑒𝑠 , 𝐸𝐼𝐴𝑐𝑐𝑜𝑚𝑜𝑑𝑎𝑡𝑖𝑜𝑛𝑈𝑛𝑖𝑡𝑠 , 𝐶𝐼𝑉𝑖𝑐𝑖𝑛𝑖𝑡𝑦 )

The urban heating energy consumption estimation is a function of mentioned elementary and

derived complex indicator. It can be given as:

𝐶𝐼𝐻𝑒𝑎𝑡𝑖𝑛𝑔𝐸𝑛𝑒𝑟𝑔𝑦𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑓(𝐸𝐼𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛𝑌𝑒𝑎𝑟 , 𝐶𝐼𝐴𝑠𝑠𝑖𝑔𝑛𝑎𝑏𝑙𝑒𝐴𝑟𝑒𝑎 , 𝐶𝐼𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔𝑇𝑦𝑝𝑒 )

Apart from heating energy consumption, heating energy can be generated within buildings.

The solar-thermal energy yield value can be directly taken from attribute field of

RoofSurface semantic element of building.

𝐸𝐼𝑅𝑜𝑜𝑓𝑆𝑜𝑙𝑎𝑟𝑇 𝑕𝑒𝑟𝑚𝑎𝑙𝑌𝑖𝑒𝑙𝑑 = 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 𝑅𝑜𝑜𝑓𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑠𝑜𝑙𝑎𝑟𝑇 𝑕𝑒𝑟𝑚𝑎𝑙𝑌𝑖𝑒𝑙𝑑


56

The remaining urban heating energy demand can be obtained by subtracting solar-thermal

energy yield from estimated heating energy consumption. The test bed proposes a heating

energy demand estimation model in terms of heating energy consumption estimation and

heating energy potential from solar energy irradiated over roof surfaces. It also provides

CityGML Energy ADE conceptual schema for heating energy consumption estimation.

6.4.2 I-SCOPE Project – Solar ADE Test Bed

Interoperable Smart City services through an Open Platform for urban Ecosystems (I-

SCOPE) Project is based on use of CityGML as Urban Information Model to provide smart

services for accurate assessment of solar energy potential at building level(Amicis R. et al.,

2012). The estimation of solar energy potential of roof is based on parameters such as

effective roof area from city model, total solar irradiation dataset stored in CityGML

textures, panel efficiency and roof orientation(Prandi et al., 2013). The single irradiation

value is assigned to roof area by overlaying corresponding areas of irradiation maps to the

roof areas(“Solar potential assessment - SmartCity Wiki,” 2013). The study provides

conceptual schema for RoofSurface Solar energy potential assessment as Solar ADE.

6.4.3 EnergyADE Proposal of the HFT Stuttgart and TU Munich

The Energy ADE proposals by HFT Stuttgart(Casper, 2014)and TU Munich(“EnergyADE

Proposal of TU Munich,” 2013) are the current developments in formulation of Energy ADE

for integration of results of different types of simulations with CityGML. The goal of HFT

Stuttgart Energy ADE is to extend CityGML data model with building energy state data for

energy refurbishment planning while Energy ADE of TU Munich deals with integration of

energy demand estimation indicators with CityGML model. The spatio-semantic information

in CityGML is important input to most of the building energy simulations and this

simulation information can also be again integrated back into the CityGML data model

through the concept of ADE. This concept of energy ADE can address the previously

mentioned interoperability gap by implementing the bidirectional workflow between design

and energy models.

The HFT Energy ADE (version 0.2, Revision 14) proposal extends AbstractBuilding and

BoundarySurface classes of CityGML to integrate the building spatial and physics

properties. It incorporates the energy simulation results such as space heating and cooling

demand, maximum heat load, etc. with AbstractBuilding class to perform building energy

diagnostics. The HFT Energy ADE XML schema definition file and example GML file

demonstrates the encoding of defined Energy ADE. The TU Munich Energy ADE provides

exhaustive indicators as input to energy demand estimation model. The overall results of

monthly and annual energy demand simulation are integrated with CityObjectGroup subclass

of AbstractCityObject CityGML class.


57

6.5 Conceptual schema for Proposed Energy ADE

In context of present research, the geometrical properties of building semantics are used as

indicators for assessment of active and passive solar potential of building by considering

seasonal variation in incident solar radiation. The AbstractBuilding class is enriched with

attributes of seasonal active solar energy production potential, annual passive electricity

savings potential and cost of photovoltaic system, etc. The AbstractBoundarySurface class is

augmented with attributes such as effective areas exposed to radiation and amount of total

incident radiation. The values for the required attributes are obtained from simulation

performed in IES Virtual Environment software. Figure 6-3 shows conceptual schema for

proposed energy ADE. The XML schema definition file of ADE schema is generated using

Enterprise Architect.

The derived CityGML instance from proposed Energy ADE schema will contain not only

spatio-semantic content but also energy simulation indicators and results. This CityGML

ADE instance can be used as common interoperable model for both design softwares and

energy simulation systems. The geometry, semantics and application specific content in

instance document can be validated against the CityGML and ADE schema in order to test

the conformance with the CityGML standard.


58

Figure 6-3: Proposed Energy ADE schema


59

7 Conclusions and Recommendations

7.1 Conclusion

The presented research has attempted to model the spatio-semantic properties of 3D urban

objects using CityGML encoding standard. The topic has been researched at urban design

scale scenario in several aspects. The study has provided detailed overview of 3D urban data

reconstruction techniques and proposed acquisition framework with reference to CityGML

LoD concept. The 3D reconstructed data of building and vegetation objects has been

encoded into CityGML standard using developed transformation methodology. The derived

CityGML model in interoperable format represents the spatio-semantic properties of urban

objects. The semantic analysis based on solar radiation simulation results have been carried

out for evaluation of energy conservation strategies in urban segment. The study has

proposed the conceptual schema for implementation of CityGML Energy ADE to

demonstrate integration of energy simulation results with spatio-semantic characteristics of

building. The integrated instance document of ADE schema could be useful for interoperable

and lossless data exchange between design and energy simulation softwares.

7.1.1 Answers to research questions

1. How to abstract and represent the 3D vector data into a semantic model of

CityGML standard?

In this research, 3D vector data has been abstracted into the five discrete CityGML LoD. The

methodologies were implemented for respective LoD reconstruction. The LoD2 and LoD3

reconstruction was automated by adopting procedural modelling approach. The complex 3D

content has been generated in ESRI CityEngine software using rule based modelling. The

generated 3D vector data has been thematically layered into building semantic components

and converted into respective CityGML classes with reference to CityGML data model. A

customised converter was developed using built-in transformers in FME to encode LoD2 and

LoD3 3D vector data into CityGML format.

2. What kind of spatio-semantic analysis possible based on geometric and semantic

properties derived from CityGML model for urban energy conservation strategies?

The solar radiation simulation was performed on 3D model and results of simulation were

integrated with semantic components of derived CityGML model. The integrated 3D model

was queried to retrieve the potential urban surfaces (facades and roofs) for deployment of

photovoltaic energy systems. The effective areas exposed to radiation were evaluated for

assessment of active solar potential of buildings. It is being observed that the seasonal

variation in incident solar radiation has not only affected the number of exposed surfaces but


60

also their effective percentage areas. The number of surfaces exposed to radiation is more in

winter than in summer because of fact that most of the buildings in study area are oriented in

east-west direction with their facades facing north-south. As sun zenith angle is more in

winter, facades receiving the radiation for longer duration are more and hence larger

effective area is under radiation.

3. How to formulate the conceptual schema for integration of application specific

information with CityGML model to allow interoperable and lossless exchange?

The conceptual schema was formulated based on solar active and passive potential indicators

of buildings. The input indicators and energy simulation results were integrated with

CityGML data model using ADE mechanism. The optimal alternative was chosen from

literature for extending CityGML with application specific properties. The proposed

CityGML Energy ADE was modelled in UML using Enterprise Architect. The XML schema

definition file of proposed ADE was generated from UML. The instance document of ADE

schema could be used as interoperable format for information exchange between design and

energy simulation systems.

7.2 Recommendations

Based on the study presented in current research, the following points can be considered for

future work:

The process of computing building dimensions from field data can be automated by

using hybrid approach. The hybrid approach will be based on processing of LiDAR

point clouds from Spaceborne and Terrestrial laser scanning for automatic derivation

of building height, facade and fenestration dimensions.

The other city objects such as traffic lights, city furniture, etc. accounting sufficient

energy consumption can be modelled in CityGML for assessment of their energy

demand and production potential.

The energy simulation can be performed with more complex urban environment

accounting effect of obstructions such as neighbourhood buildings and vegetation on

urban surfaces.

The anisotropic diffuse model can be considered for calculation of diffuse radiation

on surfaces to improve the accuracy simulation results.

The robust statistical analysis could be provided for assessment of active and passive

solar potential of buildings.

A prototype can be developed to validate both semantics and geometry of data with

CityGML standard.

The extensive RDBMS support for polyhedral data type could be provided to

facilitate the geometric computation of surface area, perimeter and volume of

polyhedral object.


61

The working database schemas can be designed and implemented for CityGML

ADE UML model.

The present research could be complemented with integration of sky illuminance

model to predict internal illumination through fenestrations for assessment of

passive solar potential of building structure at urban design scale.

7.3 Practical Application of Study

The spatially distributed urban energy demand and supply modelling requires the integration

of urban and energy planning. It involves government organizations, stakeholders, power

industries and consumers to interact with each other for decision making and energy

planning. The presented study can be used in formulation of integrated planning and robust

decision support system for urban energy infrastructure with modifications in areas of

modeling, simulation systems, and software platforms.


62

References

Albert, J., Bachmann, M., Hellmeier, A., 2003. Zielgruppen und Anwendungen für Digitale

Stadtmodelle und Digitale Geländemodelle.

Amicis R., Conti G., Patti D., Ford M., Elisei P., 2012. I-Scope–Interoperable Smart City

Services through an Open Platform for Urban Ecosystems.

Arefi, H., Engels, J., Hahn, M., 2008. Levels of Detail in 3D Building Reconstruction from

LiDAR data.

Bahu, J.M., Koch, A., Kremers, E., Murshed, S.M., 2013. Towards a 3D Spatial Urban

Energy Modelling Approach, in: ISPRS Annals of the Photogrammetry, Remote

Sensing and Spatial Information Sciences. Presented at the ISPRS 8th 3DGeoInfo

Conference, Istanbul, Turkey This contribution.

Baker, N., Hoch, D., Steemers, K., 2000. Lighting and Thermal Method.

Bartels, M., Wei, H., Mason, D.C., 2006. DTM generation from LIDAR data using skewness

balancing, in: Pattern Recognition, 2006. ICPR 2006. 18th International Conference

On. pp. 566–569.

Benner, J., Geiger, A., Gröger, G., Hafele, K., Lowner, M., 2013. Enhanced LOD Concepts

for Virtual 3D City Models, in: ISPRS Annals of the Photogrammetry, Remote

Sensing and Spatial Information Sciences. Presented at the ISPRS 8th 3DGeoInfo

Conference, Istanbul, Turkey.

Benrod, F., Bock, J., 1934. A time analysis of sunshine. Trans. Am. Illum. Eng. Soc. 34,

200–218.

Biljecki F., Zhao J., Sototer J., Ledoux H., 2013. Revisiting the Concept of Level of Detail in

3D City Modelling, in: ISPRS Annals of the Photogrammetry, Remote Sensing and

Spatial Information Sciences. Presented at the ISPRS 8th 3DGeoInfo Conference,

Istanbul, Turkey.

Biljecki, I.F., 2013. The concept of level of detail in 3D city models (PhD Research Proposal

No. GISt Report No. 62).

Breunig, M., Zlatanova, S., 2011. 3D geo-database research: Retrospective and future

directions. Comput. Geosci. 37, 791–803. doi:10.1016/j.cageo.2010.04.016

Brugman, B., 2010. 3D topological structure management within a DBMS. Delft University

of Technology Institutional Repository.

Brunetaud, X., Stefani, C., Badosa, S.J., Beck, K., Al-Mukhtar, M., 2012. Comparison

between photomodelling and laser scanning to create a 3D model for a digital health

record. Eur. J. Environ. Civ. Eng. 16, s48–s63. doi:10.1080/19648189.2012.681957

buildingSMART, 2007. Industry Foundation Classes IFC2x3 TC1 Release.

Carrión, D., Lorenz, A., Kolbe, T.H., 2010. Estimation of the energetic rehabilitation state of

buildings for the city of Berlin using a 3D city model represented in CityGML. Int.

Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38, 31–35.

Casper, E., 2014. EnergyADE Proposal of HfT Stuttgart [WWW Document].

2014_S_HfT_EnergyADE.URL

http://en.wiki.modeling.sig3d.org/index.php?title=2014_S_HfT_EnergyADE&oldid

=494 (accessed 6.14.14).

Coulson, K.L., 1975. Solar and Terrestrial Radiation. Academic press, New York.

DeWitt, B.A., Wolf, P.R., 2000. Elements of Photogrammetry(with Applications in GIS),

3rd ed. McGraw-Hill Higher Education.


63

Duffie, J., Beckman, W., 1980. Solar Engineering of Thermal Processes, Second. ed. John

Wiley & Sons, Inc.

Eicker, U., Nouvel, R., Schulte, C., Schumacher, J., Coors, V., 2012. 3D-STADTMODELLE

FÜR DIE WÄRMEBEDARFBERECHNUNG, in: BauSIM. Presented at the Fourth

German-Austrian IBPSA Conference.

El-Mekawy, M., Östman, A., Hijazi, I., 2012. A Unified Building Model for 3D Urban GIS.

Isprs Int. J. Geo-Inf. 1, 120–145. doi:10.3390/ijgi1020120

EnergyADE Proposal of TU Munich [WWW Document], 2013. URL

http://en.wiki.modeling.sig3d.org/images/upload/20140519_EnergyADE_TUM.pdf

(accessed 6.14.14).

Goetz, M., 2013. Towards generating highly detailed 3D CityGML models from

OpenStreetMap. International Journal of Geographic Information Science. 27, 845–

865. doi:10.1080/13658816.2012.721552

Goetz, M., Zipf, A., 2012. Towards Defining a Framework for the Automatic Derivation of

3D CityGML Models from Volunteered Geographic Information. Int. J. 3- Inf.

Model. Volume 1, 1–16.

Gröger, G., Plümer, L., 2012. CityGML – Interoperable semantic 3D city models. Isprs J.

Photogramm. Remote Sens. 71, 12–33. doi:10.1016/j.isprsjprs.2012.04.004

Isikdag, U., Zlatanova, S., 2009. Towards defining a framework for automatic generation of

buildings in CityGML using building Information Models, in: 3D Geo-information

Sciences. Springer, pp. 79–96.

Kaden, R., Kolbe, T.H., 2013. City-Wide Total Energy Demand Estimation of Buildings

using Semantic 3D City Models and Statistical Data, in: ISPRS Annals. Presented at

the ISPRS 8th 3DGeoInfo Conference, Istanbul, Turkey, pp. 163–171.

Kalantaitė, A., Parseliūnas, E.K., Romanovas, D., Slikas, D., 2012. Generating the open

space 3D model based on LiDAR data. Geod. Cartogr. 38, 152–156.

Kolbe, T.H., 2009. Representing and exchanging 3D city models with CityGML, in: 3D

Geo-information Sciences. Springer, pp. 15–31.

Kolbe, T.H., Gerhard Gröger, Claus Nagel, Karl-Heinz Häfele (Eds.), 2012. OGC City

Geography Markup Language (CityGML) En-coding Standard.

Krüger A., Kolbe T.H., 2012. Building Analysis For Urban Energy Planning Using Key

Indicators On Virtual 3D City Models - The Energy Atlas Of Berlin, in:

International Archives of the Photogrammetry, Remote Sensing and Spatial

Information Sciences. Presented at the XXII ISPRS Congress, Melbourne, Australia.

Lee, J., Zlatanova, S., 2009. Solar radiation over the urban texture: LIDAR data and image

processing techniques for environmental analysis at city scale, in: 3D Geo-

Information Sciences. Springer, pp. 319–340.

Liu, B.Y.H., Jordan, R.C., 1963. The Long-Term Average Performance of Flat-Plate Solar-

Energy Collectors. Solar Energy 7, 53–74.

Luebke, D., Reddy, M., Cohen, J.D., Varshney, A., Watson, B., Huebner, R., 2003. Level of

detail for 3D graphics, morgan kaufmann publishers ed. ed. Morgan Kaufmann Pub.

Nguyen, T.T., Nguyen, Q.M., Liu, X.G., Ziggah, Y.Y., 2012. 3D Object Model

Reconstruction based on LASER Scanning Point Cloud Data. Int. Symp.

Geoinformatics Spat. Infrastruct. Dev. Earth Allied Sci.

Oda, K., Takano, T., Doihara, T., Shibasaki, R., Automatic Building Extraction and 3-D City

Modeling from LIDAR data based on Hough Transformation.

Perez, Richard, R., Stewart, R., Guertin, T., 1988. The development and verification of the

Perez diffuse radiation model. (No. No. SAND-88-7030). Atmospheric Sciences


64

Research Center,Sandia National Labs., Albuquerque, NM (USA);State Univ. of

New York, Albany (USA).

Prandi, F., De Amicis, R., Piffer, S., Soavea, M., Cadzowb, S., Boix, E.G., D‟Hondt, E.,

2013. Using CityGML To Deploy Smart-City Services For Urban Eco-Systems.

Ratti, C., Baker, N., Steemers, K., 2005. Energy consumption and urban texture. Energy

Build. 37, 762–776. doi:10.1016/j.enbuild.2004.10.010

RHYNE, T.-M., 1999. A commentary on GeoVRML: a tool for 3D representation of

georeferenced data on web. Int. J. Geogr. Inf. Sci. 439–443.

Richardson, D.E., 2002. 3D GIS, where are we standing?, in: Advances in Spatial Data

Handling. Springer, Berlin.

Rolf A. de, Knippers, R., Sun, Y., Ellis, M., Kraak, M.-J., Weir, M., Georgiadou, Y.,

Radwan, M., Westen, C., Kainz, W., Sides, E., 2001. Principles of geographic

information systems: an introductory textbook. International Institute for Aerospace

Survey and Earth Sciences, Enschede, The Netherlands.

Sarak, H., Satman, A., 2003. The degree-day method to estimate the residential heating

natural gas consumption in Turkey: a case study. Energy Volume 28, 929–939.

Sirmacek, B., Taubenboeck, H., Reinartz, P., 2012. A Novel 3D City Modeling approach for

satellite stereo data using 3D active shape models on DSMS, in: International

Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,

XXII ISPRS Congress. Melbourne, Australia, pp. 325–330.

Solar potential assessment - SmartCity Wiki [WWW Document], 2013. URL

http://smartcity.gistandards.eu/index.php/Solar_potential_assessment (accessed

11.30.13).

Stadler, A., Kolbe, T.H., 2007. Spatio-semantic coherence in the integration of 3D city

models, in: Proceedings of the 5th International Symposium on Spatial Data Quality,

Enschede.

Strzalka, A., Eicker, U., Coors, V., Schumacher, J., 2010. Modeling Energy Demand for

Heating at City Scale, in: SimBuild–Fourth National Conference. New York:

IBPSA-USA. pp. 358–364.

Tang, T., Zhao, W., Gong, H., Zhang, A., Pan, J., Liu, Z., 2008. Terrestrial laser scan survey

and 3D TIN model construction of urban buildings in a geospatial database.

Geocarto Int. 23, 259–272. doi:10.1080/10106040801915917

Tereci, A., Schneider, D., Kesten, D., Strzalka, A., Eicker, U., 2009. Energy Saving Potential

and Economical Analysis of Solar Systems in Urban Quarter Scharnhaauser Park.

Presented at the ISES Solar World Congress 2009 : Renewable energyShaping our

Future.

Van den Brink, L., Stoter, J., Zlatanova, S., 2013. UML-Based Approach to Developing a

CityGML Application Domain Extension. Trans. Gis 17, 920–942.

doi:10.1111/tgis.12026

Wate, P., Saran, S., Srivastav, S.K., Murthy, Y.V.N.K., 2013. Formulation of Hierarchical

Framework for 3D-GIS Data Acquisition Techniques in context of Level-of-Detail

(LoD), in: 2013 IEEE Second International Conference on Image Information

Processing (ICIIP-2013). IEEE, Shimla, India, pp. 154–159.

doi:10.1109/ICIIP.2013.6707573

Zhang, Y., Zhu, Q., Liu, G., Zheng, W., Li, Z., Du, Z., 2011. GeoScope: Full 3D geospatial

information system case study. Geo-Spat. Inf. Sci. 14, 150–156.

doi:10.1007/s11806-011-0478-z


65

Zlatanova, S., 2000. 3D GIS for urban development. International Institute for Aerospace

Survey and Earth Sciences ; Institute for Computer Graphics and Vision, Graz

University of Technology, Enschede, Netherlands; Graz, Australia.

Zlatanova, S., Rahman, A., Pilouk, M., 2002. 3D GIS: current status and perspectives. Int.

Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34, 66–71.

Zlatanova, S., Rahman, A.A., Shi, W., 2004. Topological models and frameworks for 3D

spatial objects. Comput. Geosci. 30, 419–428. doi:10.1016/j.cageo.2003.06.004

Zlatanova, S., Stoter, J., Isikdag, U., 2012. Standards for exchange and storage of 3D

information: Challenges and opportunities for emergency response, in: Proceedings

of the Fourth International Conference on Cartography and GIS, Albena, Bulgaria.

pp. 17–28.


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APPENDIX 1

Figure A1-1: LoD3 schema of Gymnasium Building Rule File

Documents

3D GIS Modeling at Semantic Level using CityGML for … · vi Declaration I, Parag Sudhir Wate, hereby declare that this dissertation entitled “3D GIS Modeling at Semantic Level