MODELING 3D BUILDINGS OF LOD2 FROM AIRBORNE POINT CLOUD USING
UNSUPERVISED CLASSIFICATION
SETHU MADHAVAN JANARTHANAN
A project report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Science (Geoinformatics)
Faculty of Geoinformation and Real Estate
Universiti Teknologi Malaysia
AUGUST 2011
iii
ACKNOWLEDGEMENT
It is my great privilege to express gratitude to my supervisor Prof. Dr. Alias
Abdul Rahman, 3D GIS Research Lab and Dean of Faculty of Geoinformation and Real
Estate for giving me the freedom to do my work at my own pace. I am deeply beholden
for the constant encouragement and support to bring out my ideas and channelize them.
I am extremely thankful to Mr. Alex Rixon, Production Manager, AAM Group,
Kuala Lumpur for helping me in this study by giving the data, software and his valuable
ideas and allowing me to work in his office. I would also like to thank Mr. Ahmad
Syuwari at AAM for helping me with the technical issues.
I wish my deep sense of gratitude to all the members of the 3D GIS Research
Group for their support and sharing thoughts throughout my study. I would also like to
thank the staff members of the Faculty of Geoinformation and Real Estate for their help
and assistance. I would like to thank Wasiu, Toyin and all my postgraduate classmates
and friends for their constant encouragement and support in one way or the other
throughout my study.
Finally, I wish to express my heartfelt gratitude and love to my Mom, Dad and
Brother for being with me right through my work and helping me in all possible ways.
iv
ABSTRACT
A variety of applications utilize 2D data in some form or the other to complete
their tasks. But we are living in a 3D world and in most cases 2D information is not
sufficient. Today the need for 3D Geoinformation has increased rapidly mainly because
there is a significant improvement in maintaining, processing and visualizing these data.
A variety of applications have been introduced in relation to visualization like a 3D city
model. A 3D city model includes buildings, vegetation, street furniture and other city
objects. 3D city models can be generated from various sources of data like aerial
images, CAD, satellite imagery, LiDAR and terrestrial laser scan. But LiDAR and
terrestrial laser scan holds as the best source of data in terms of accuracy. With LiDAR
accurate 3D models can be generated when compared to other conventional method like
the photogrammetric technique. The data is collected as set of points called as point
cloud. MicroStation with extension TerraScan was used to process these 3D point
clouds from which the 3D models and the 3D surface model (DTM) were generated.
This study aims to generate 3D city model from the airborne LiDAR and incorporate
them in CityServer3D where the 3D geodatabase is created. All the generated models
are based on the standard CityGML format. Each building is given an external code
based on the CityGML format defined by the Open Geospatial Consortium (OGC). The
models inside the CityServer3D can be visualized as well as queried. The Level of
Detail of the 3D models is restricted to 2 without façade textures. This 3D city model
will be of good use to the local authorities of Miri during times of flood because the
study area is located relatively close to a river meeting the sea. This 3D city model can
be improved by adding textures, increasing the level of detail which will be more virtual
and realistic.
v
ABSTRAK
Pada masa kini, terdapat pelbagai aplikasi di dalam bentuk 2D digunakan di
dalam menyelesaikan tugasan. Bagaimanapun, maklumat di dalam bentuk 2D sahaja
tidak mencukupi memandangkan persekitaran di sekeliling berbentuk 3D. Kini,
keperluan kepada geoinformasi berbentuk 3D telah meningkat kerana keupayaannya di
dalam mengekalkan, memproses dan menggambarkan sesebuah data. Pelbagai aplikasi
telah diperkenalkan di dalam menvisualisasikan sesebuah bandar di dalam bentuk 3D.
Model 3D bandar termasuklah bangunan, tumbuhan, perabot jalan dan objek bandar
yang lain. Model 3D bandar boleh dijana daripada pelbagai sumber data seperti imej
udara, CAD, imej satelit, LiDAR dan imbasan terrestrial laser. Bagaimanapun sumber
dari LiDAR dan imbasan terrestrial laser lebih tepat dan jitu. Model 3D yang dihasilkan
dari LiDAR lebih tepat berbanding menggunakan kaedah konvensional seperti teknik
fotogrametri. Set data yang dikumpul dikenali sebagai titik awan. Microstation dengan
sambungan TerraScan digunakan di dalam proses 3D titik awan ini di mana model 3D
dan permukaan 3D (DTM) akan dihasilkan. Kajian ini bertujuan untuk menjana model
3D bandar berasaskan data LIDAR udara dan menggabungkan data menggunakan
perisian CityServer3D di mana akan membentuk geodatabase bagi model 3D. Model-
model yang dihasilkan adalah mengikut piawaian dan format CityGML. Setiap
bangunan akan dinamakan dengan kod luar berdasarkan format CityGML yang
ditakrifkan oleh Open Geospatial Consortium (OGC). Model yang dihasilkan dari
CityServer3D boleh digambarkan dan tahap perincian model 3D yang dihasilkan terhad
kepada dua permukaan iaitu hadapan dan tekstur bangunan. Penghasilan model 3D ini
seterusnya dapat membantu Pihak Berkuasa Tempatan Miri di dalam mengatasi masalah
banjir kerana kawasan kajian terletak berhampiran dengan muara sungai. Model 3D
bandar ini boleh diperbaiki dengan menambah tekstur, bagi meningkatkan tahap
perincian agar model yang dihasilkan lebih realistik.
vi
TABLE OF CONTENTS
CHAPTER TITLE PAGE
TITLE i
DECLARATION ii
ACKNOWLEDGEMENTS iii
ABSTRACT iv
ABSTRAK v
TABLE OF CONTENTS vi
LIST OF FIGURES x
LIST OF ABBREVIATIONS xi
1. INTRODUCTION 1
1.1 General Introduction 1
1.1.1 Visualization 3
1.1.2 3D GIS 4
1.2 Background 6
1.3 Problem Statement 8
1.4 Aim 8
1.5 Objectives 9
1.6 Scope 9
vii
1.6.1 Study Area 9
1.6.2 Data 10
1.6.3 Software 11
1.7 Significance 12
1.8 Chapter Organization 12
2. LITERATURE REVIEW 14
2.1 Introduction 14
2.2 Overview of 3D City Modeling 15
2.3 CityGML 16
2.3.1 CityGML Features 17
2.3.2 Background 18
2.3.3 Level of Detail (LoD) 19
2.4 LIDAR 21
2.5 Terrestrial Laser Scanner 26
2.6 3D City and Urban Modeling 29
2.6.1 City Objects 29
2.6.2 Buildings 30
2.7 MicroStation 31
2.7.1 TerraScan 31
2.7.1.1 Filtering the Point Cloud 32
2.7.1.2 Project and Macros 33
2.7.1.3 Features 33
2.8 Google SketchUp 35
2.9 CityServer3D 36
2.10 Google Earth 36
2.10.1 Buildings in 3D 37
2.11 Softwares for working with LiDAR data 37
2.12 Related Works 40
viii
2.13 Summary 43
3. METHODOLOGY 44
3.1 Introduction 44
3.2 Project Methodology 44
3.3 Data Processing Stage 46
3.3.1 Data Pre-processing 47
3.3.1.1 Noise Filtering 47
3.3.2 Data Post-processing 48
3.4 Data Classification 49
3.5 Data Modeling 51
3.5.1 Generating Surface Model (DTM) 52
3.5.1.1 Accuracy of the DTM 54
3.5.2 Reconstruction of 3D building 56
3.6 Data Visualization 57
3.6.1 Exporting models into CityGML 58
3.6.2 Models inside CityServer3D 59
3.7 Summary 60
4. RESULTS AND DISCUSSION 61
4.1 Introduction 61
4.2 Analyzing the models generated from
MicroStation 61
4.2.1 3D Building Models 62
4.3 Models inside Google Earth 64
4.4 Models inside CityServer3D 64
4.5 Recommendation 66
ix
4.6 Summary 66
5. CONCLUSION AND RECOMMENDATION 67
5.1 Introduction 67
5.2 Conclusion 67
5.3 Problems and Limitations 68
5.4 Recommendation 69
REFERENCES 70
x
LIST OF FIGURES
FIGURE NO TITLE PAGE
Figure 1.1 Study area- Satellite image of part of the Miri City 10
Figure 1.2 LiDAR point cloud of the study area-Miri 11
Figure 2.1 The five levels of detail (LOD) defined by CityGML 20
Figure 2.2 Airborne LiDAR system 23
Figure 2.3 Example of an airborne point cloud 24
Figure 2.4 Example of a terrestrial point cloud 27
Figure 3.1 The Project workflow 45
Figure 3.2 Point cloud data of Miri city 46
Figure 3.3 Noise data 48
Figure 3.4 Noise removed data 48
xi
Figure 3.5 Data classification method 50
Figure 3.6 Segmented point cloud data 51
Figure 3.7 Triangular mesh of the point cloud 53
Figure 3.8 Surface model of the DTM 53
Figure 3.9 Cross-section view of the 3D model with DTM 54
Figure 3.10 Test point results 55
Figure 3.11 Connecting edge segments 56
Figure 3.12 Final model 56
Figure 3.13 Connected edge segments resulting in 3D models 56
Figure 3.14 The complete model in Google SketchUp 57
Figure 3.15 CityGML file as seen in CityGML editor 58
Figure 3.16 3D building models (LoD2) inside CityServer3D 59
Figure 3.17 Adding metadata for a building 59
Figure 3.18 External code list for building class type 60
Figure 4.1 Generated 3D building model overlaid on top
of the point cloud 62
xii
Figure 4.2 LiDAR point cloud of the study area 63
Figure 4.3 Generated 3D model of the study area 63
Figure 4.4 3D city model of Miri inside Google Earth 64
Figure 4.5 3D city model of Miri inside CityServer3D 65
Figure 4.6 CityGML file of a building model 65
xiii
LIST OF ABBREVIATIONS
2.5D - Two-and-a-half-dimensional
2D - Two-dimensional
3D - Three-dimensional
ALDPAT - Airborne LIDAR Data Processing and Analysis Tool
ALS - Airborne Laser Scanning
B-REP - Boundary Representation
CAD - Computer Aided Design
CSG - Constructive Solid Geometry
DBM - Digital Building Model
DEM - Digital Elevation Model
DSM - Digital Surface Model
DTM - Digital Terrain Model
GIS - Geographic Information System
GML - Geographic Markup Language
GPS - Global Positioning System
INS - Inertial Navigation Sensor
LIDAR - Light Detection And Ranging
LoD - Level of Detail
OGC - Open Geospatial Consortium
TIN - Triangulated Irregular Network
TLS - Terrestrial Laser Scanning
USDA - United States Department of Agriculture
USGS - United States Geological Survey
xiv
VRML - Virtual Reality Markup Language
W3DS - Web 3D Service
WGS - World Geodetic System
vi
TABLE OF CONTENTS
CHAPTER TITLE PAGE
TITLE i
DECLARATION ii
ACKNOWLEDGEMENTS iii
ABSTRACT iv
ABSTRAK v
TABLE OF CONTENTS vi
LIST OF FIGURES x
LIST OF ABBREVIATIONS xi
1. INTRODUCTION 1
1.1 General Introduction 1
1.1.1 Visualization 3
1.1.2 3D GIS 4
1.2 Background 6
1.3 Problem Statement 8
1.4 Aim 8
1.5 Objectives 9
1.6 Scope 9
vii
1.6.1 Study Area 9
1.6.2 Data 10
1.6.3 Software 11
1.7 Significance 12
1.8 Chapter Organization 12
2. LITERATURE REVIEW 14
2.1 Introduction 14
2.2 Overview of 3D City Modeling 15
2.3 CityGML 16
2.3.1 CityGML Features 17
2.3.2 Background 18
2.3.3 Level of Detail (LoD) 19
2.4 LIDAR 21
2.5 Terrestrial Laser Scanner 26
2.6 3D City and Urban Modeling 29
2.6.1 City Objects 29
2.6.2 Buildings 30
2.7 MicroStation 31
2.7.1 TerraScan 31
2.7.1.1 Filtering the Point Cloud 32
2.7.1.2 Project and Macros 33
2.7.1.3 Features 33
2.8 Google SketchUp 35
2.9 CityServer3D 36
2.10 Google Earth 36
2.10.1 Buildings in 3D 37
2.11 Softwares for working with LiDAR data 37
2.12 Related Works 40
viii
2.13 Summary 43
3. METHODOLOGY 44
3.1 Introduction 44
3.2 Project Methodology 44
3.3 Data Processing Stage 46
3.3.1 Data Pre-processing 47
3.3.1.1 Noise Filtering 47
3.3.2 Data Post-processing 48
3.4 Data Classification 49
3.5 Data Modeling 51
3.5.1 Generating Surface Model (DTM) 52
3.5.1.1 Accuracy of the DTM 54
3.5.2 Reconstruction of 3D building 56
3.6 Data Visualization 57
3.6.1 Exporting models into CityGML 58
3.6.2 Models inside CityServer3D 59
3.7 Summary 60
4. RESULTS AND DISCUSSION 61
4.1 Introduction 61
4.2 Analyzing the models generated from
MicroStation 61
4.2.1 3D Building Models 62
4.3 Models inside Google Earth 64
4.4 Models inside CityServer3D 64
4.5 Recommendation 66
ix
4.6 Summary 66
5. CONCLUSION AND RECOMMENDATION 67
5.1 Introduction 67
5.2 Conclusion 67
5.3 Problems and Limitations 68
5.4 Recommendation 69
REFERENCES 70
x
LIST OF FIGURES
FIGURE NO TITLE PAGE
Figure 1.1 Study area- Satellite image of part of the Miri City 10
Figure 1.2 LiDAR point cloud of the study area-Miri 11
Figure 2.1 The five levels of detail (LOD) defined by CityGML 20
Figure 2.2 Airborne LiDAR system 23
Figure 2.3 Example of an airborne point cloud 24
Figure 2.4 Example of a terrestrial point cloud 27
Figure 3.1 The Project workflow 45
Figure 3.2 Point cloud data of Miri city 46
Figure 3.3 Noise data 48
Figure 3.4 Noise removed data 48
xi
Figure 3.5 Data classification method 50
Figure 3.6 Segmented point cloud data 51
Figure 3.7 Triangular mesh of the point cloud 53
Figure 3.8 Surface model of the DTM 53
Figure 3.9 Cross-section view of the 3D model with DTM 54
Figure 3.10 Test point results 55
Figure 3.11 Connecting edge segments 56
Figure 3.12 Final model 56
Figure 3.13 Connected edge segments resulting in 3D models 56
Figure 3.14 The complete model in Google SketchUp 57
Figure 3.15 CityGML file as seen in CityGML editor 58
Figure 3.16 3D building models (LoD2) inside CityServer3D 59
Figure 3.17 Adding metadata for a building 59
Figure 3.18 External code list for building class type 60
Figure 4.1 Generated 3D building model overlaid on top
of the point cloud 62
xii
Figure 4.2 LiDAR point cloud of the study area 63
Figure 4.3 Generated 3D model of the study area 63
Figure 4.4 3D city model of Miri inside Google Earth 64
Figure 4.5 3D city model of Miri inside CityServer3D 65
Figure 4.6 CityGML file of a building model 65
xiii
LIST OF ABBREVIATIONS
2.5D - Two-and-a-half-dimensional
2D - Two-dimensional
3D - Three-dimensional
ALDPAT - Airborne LIDAR Data Processing and Analysis Tool
ALS - Airborne Laser Scanning
B-REP - Boundary Representation
CAD - Computer Aided Design
CSG - Constructive Solid Geometry
DBM - Digital Building Model
DEM - Digital Elevation Model
DSM - Digital Surface Model
DTM - Digital Terrain Model
GIS - Geographic Information System
GML - Geographic Markup Language
GPS - Global Positioning System
INS - Inertial Navigation Sensor
LIDAR - Light Detection And Ranging
LoD - Level of Detail
OGC - Open Geospatial Consortium
TIN - Triangulated Irregular Network
TLS - Terrestrial Laser Scanning
USDA - United States Department of Agriculture
USGS - United States Geological Survey
xiv
VRML - Virtual Reality Markup Language
W3DS - Web 3D Service
WGS - World Geodetic System
REFERENCES
Abdul-Rahman. A.. and Pilouk. M. (1008). Spatial Data Modeling for 3D GIS,
Chapter 1. Introd uction. \\'by does 3D GIS matt er? pp. 1-2. ISBN 978-3-540
74 166·4 Springer Berlin Heidelbe rg New " ork.
Arefi . H.. Engels. J.• Hahn. ~1 .. and Mayer. H. C~008 ) . Levels of Detail in 3D
Building Reconstruction from LiDAR Data. The Internationa l Archives of the
Phorogrammetry. Remote Sensing and Spatial Infonn ation Sciences. Vol.
XXXVII. Pan B3b. Beijing.
Baligh. B.. Valadan Zoej . M J .. and Moharnmadzadeh. A. (2008). Bare Earth
Extraction from Airborne L1DAR Data Using Different Filteri ng Methods.
ISPRS/\\"G III Processing of Point Clouds from Laser Scanners and Other
Sensors.
Barte ls. M.. and Wei. H. C!OO61 . DT M Generation from U DAl{ Data Us ing
Skewness Balancing.18th International Conference on Pattern Recognition.
1:566-569. Hong Kong, China. 21-24 August ~006 .
Bourke . P. (1989) Triangulate: Efficie nt Triangulation Algorithm Suitable I'm
Terrain Modeling. Pan Pacifi c Computer Conference.
Brenner, C. (2000 ). To ward s Fully Automa tic Gene rat ion of City Model s. XIX
ISPRS Congress IAP RS. Amsterdam. the Netherlands . pp. X5-9~ .
Gar land. P.• and Heckbert, P. (1997). Surface Simplification Using Quadratic I-:rWI"
Metri co. AC M SIGG RA PH (pp. 209 ·216).
71
Gone, B., and Pfeifer, N. (2()()4). Structuring Laser-scanned Trees Using 3D
Mathematical Morphology. International Archives of Photogrammetry and
Remote Sensing. Vol. XXXV. Istanbul. Turkey.
Hammoudi. K., Domaika. F.. Soheilian. B.. and Paparoditis, N. (2010). Extracting
Wire-frame Models of Street Facades from 3D Point Clouds and the
Corresponding Cadastral Map. International Archive s of Photogrammetry,
Remote Sensing and Spatial Information Sciences (lAPRS). \'01. 38, part 3A. pp.
91-96. Saint-Mande. France. 1-3 September 2010.
Hamm oud., K.. and Domaika, F. A. Featureless Approach to 3D Polyhedral
Building Modeling from Aerial Images. Sensors. 201 1: I I( I):228-259.
Kolbe. I .H. (2010) . Tutorial on 3D City Modeling. University Teknologi Malay sia
Skudai. Jobor. Malaysia. Septembe r 23·24 .
Kolbe, T. B.. Greger. G.. and Plumer. L. (2005). Citygml interoperable access 10 3d
city models. In: F. E. Oosterorn. Zlatonova (ed.). Proceedings of International
Symposium on Geo- infonnation for Disaster Management, 21-23 March 2005 in
Delft. Springer Verlag .
Kraus. K., and Pfeifer, N. (2001). Advanced DTM generation from LiDAR data.
International Archives of Photogrammetry and Remote Sensing. Volume
XXX!V- 3/W4 Annapol is. MD. 22-24 Oct. 2001
Li. Y.• and Yang, L. (2009) . Based on Delaunay Triangulation DEM of Terrain
Model. Computer and Information Science, Vol.Z, No.2.
'WWW.ccsenet.orgljoumaLhtmL
Maas, H.G. (200 1). The Suitability of Airborne Laser Scanner Data for Automatic
3D Object Reconstruction, Third International Workshop on Automatic
Extraction of Man-Made Objects from Aerial and Space Images , Ascona..
Switzerland.
Nalani, H.A., and Hahn. M. (2007). An Interact ive Tool for 3D Object Extraction
Using Airborne Laser Scanning Data and Orthcimages, ISPRS, Volume XXXV I
- 4fW45, Joint Workshop "Visualization and Exploration of Geospatia l Data".
JW1e 27-29, 2007, Stuttgart (Germany) .
Pu, S., and Vosselman. G. (2006). Automatic extraction of building features from
terrestrial laser scanning, International Archives of Photogrammetry, Remote
Sensing and Spatial Information Sciences. vol. 36. part 5, Dresden , Germany,
September 25·27, 5 p.
Pu, S. (2008 ). Automatic building modeling from terrestrial laser scanning, In P. Van
Oostercm, S. Zlatanova, F. Penninga, E. Fendel (eds). Advances in 3D
Geoinformation Systems, LNG&C, Springer, pp. 146-160.20
Rabbani, T., Heuvel. F.A., and Vosselman. G. (2006) . Segmentation of Point Clouds
Using Smoothness Constraints. International Archives of Photogrammetry,
Remote Sensing and Spatial Information Sciences, vol. 36, pan 5, Dresden,
Germany, September 25-27, pp. 248-253.
Robinson, A.H., and Petchenik, B.B. (1976). The Nature of Maps. Chicago:
University of Chicago Press.
Regnant, L., Planes, J.G., Memier, M., and Chassery, J.M. (2001). Contour Lines
and DEM: Generation and Extraction . Lecture Notes in Computer Science, 2001,
Volume 2181 /2001 . 87-97. DOl : 10.100713-540-4481 8-7_13 .
Rortensteiner. F., and Briesse, Ch. (2003). Automatic Generation of Building Models
from LiDAR Data and the Integration of Aerial Images. ISPRS, Vol. XXXIV,
Dresden.
Rusinkiewicz, S., and Levey, M. (2000) . QSplat: a muitiresolution point rendering
system for large meshes . In Siggraph 2000. ACM, New York, NY, 343·352.
DOI= hnp:Jldoi.acm.orglIO.l145/344779.344940.
r73
Sohn, G., and Dowman, I.J. (2004). Extraction of buildings from high resolution
satellite data and LIDAR . ISPRSI WGm Automated Object Extraction.
Sitek, A., Huesman, R.H ., and Gullberg. G.T. (2006). "Tomographic Reconstruction
Using an Adaptive Tetrahedral Mesh Defined by a Point Cloud" IEEE Trans.
Moo. Imag. 25 11 72.
Stadle r, A., and Kolbe, T. H. (2007). Spatia-Se mantic Coherence in the Integration
of 3D City Models; Proceedings of 5th International ISPRS Symposium on
Spatial Data Quality ISSDQ 2007 in Enschede .
Sulaiman, N.S., Maj id, Z., and Setan, H. (20 10). DTM generation from LiDAR data
by using different filters in open-source software, Geoinfonnation Science
Journal. Vol. 10, No.2, 20 10, pp: 89-109.
Suveg, I., and Vosselman, G. (2004). Reconstruction of 3D Building Models from
Aerial Images and Maps. ISPRS Journal of Photogrammetry and Remote
Sensing 58 (3-4) , p202 -224.
Tao. C., and IIu, Y. (2001). A review of post-processing algorithms for airborne
LiDAR data . CD·ROM Proceedings of ASPRS Annual Conference, April 23- 27,
St. Louis, Missouri, USA.
Tse, R.O.C., Gold, C.M., and Kidner, D. (2006). A New Approach to Urban
Modelling Based on LiDAR, Conference proceedings ISBN 80~86943 ·03 ·8 ,
WSCG ' 2005, January 30-February 3, 2006, Plezn, Czech Republic.
Vosse lman, G., and Dijkman, S. (200 1). 3D Building Model Reconstruction from
Point Clouds and Ground Plans, International Archives of Photogrammetry and
Remote Sensing, Volwne XXXIV-3/W4 Annapolis, MD, 22-24 Oct.
Vosselman, G., Gon e, B., Sithole, G., and Rabbani, T. (2004). Recognizing
Structure in Laser Scanner Point Clouds, International Conference NATSCAN,
74
Laser-Scanners for Fares and Landscape. Processing Methods and Applications.
ISP RS working group V1I1I2. Freiburg im Breisgau, Germany.
Woo, H., Kang. E., Wang, S., and Kwan H. Lee. (1002). A New Segmentation
Method for Point Cloud Data. International Journal of Machine Tools and
Manufacture. January, vol. 42, no. 2. pp. 167-178 (12)
Xiaoye, L. (2008). Airborne LiDAR for OEM generation: some critical issues.
Progress in Physical Geography, Vol. 32 ( I), 31-49.
Zhou. G., Song. C.• Simmers , J.. and Cheng. P. (2004). Urban 3D GIS From LiDAR
and digital aerial images. Computers & Geosciences. pp. 345-353.
Zlatanova, S,; Abdul-Rahman. A., Pilouk. M. (2002). 3D GIS: Current Status and
Perspectives. Proc. of the Joint Conference on Goo-spatial theory, Processing and
Applications. 8-12 July, Ortawa, Canada.
