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21/4/2015 Dr. Ahmad Yousef
The Use of GIS for 3D Urban Models
Reconstruction Based on Aerial Lidar Data
Dr. Ahmad Yousef
21/4/2015 Dr. Ahmad Yousef
• Overview
• Objectives & Goals
• Digital Terrain Model Extraction
• 3D Building Model Reconstruction
• Conclusion & Final Thoughts
AGENDA
21/4/2015 Dr. Ahmad Yousef
Pollution, Cairo
Visualization, Dubai
Pollution, Moscow
Flood, Pakistan
Visualization, Los Angeles
City Planning, Stuttgart
Flood, Mexico
• GIS has traditionally been 2D technology.
• New product and technology make us reconsider the role of 3D.
• It is widely recognized that 3D models are necessary.
Overview
Why 3D GIS …
21/4/2015 Dr. Ahmad Yousef
Year : 2050
66%
Year : 2014
54%
Urban Population
World
Source: UN DESA’s Population Division
of Global Population
o The overall growth of the
world’s population could add
another 2.5 billion people to
urban populations by 2050
o 90 percent of the increase
concentrated in Asia and
Africa
Overview
Why urban area …
21/4/2015 Dr. Ahmad Yousef
Overview
Why lidar data …
Waldkirch, Germany
21/4/2015 Dr. Ahmad Yousef
Overview
Workshop Objectives & Goals
21/4/2015 Dr. Ahmad Yousef
Overview
Workshop Objectives & Goals
21/4/2015 Dr. Ahmad Yousef
Overview
Workshop Objectives & Goals
1. Introduce a fast and simple integrated Digital Terrain Model (DTM)
extraction framework in a GIS environment.
21/4/2015 Dr. Ahmad Yousef
Overview
Workshop Objectives & Goals
1. Introduce a fast and simple integrated Digital Terrain Model (DTM)
extraction framework in a GIS environment.
2. Introduce a developed GIS approach for reconstructing of the 3D building
models from lidar point clouds.
21/4/2015 Dr. Ahmad Yousef
Digital Terrain Model Extraction
How to classify lidar data into terrain and off-terrain points?
21/4/2015 Dr. Ahmad Yousef
• Filtering means classification of points into terrain and off-terrain.
• Bare earth is assumed to be continuous surface.
• Filtering In/Out data:
– Point list
– Grid
– Triangulated Irregular Network - TIN
DTM Extraction
Filtering lidar data
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Esri Terrain Dataset
Surface geometry at different resolution (TINs)
– TIN is acronym for triangulated irregular network.
Level of Detail-Pyramid
LoD x
Pyramid Type:
I. WINDOWSIZE
II. ZTOLERANCE
parameter:
I. ZMIN
II. ZMAX
III. ZMEAN
IV. ZMINMAX
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Esri Terrain Dataset
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Esri Terrain Dataset
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Esri Terrain Dataset
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Esri Terrain Dataset
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Minimum Block Classification - MBC
Window Block
Bare Earth Surface
Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance)
Output Ground Points, DTM
Window Block
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Minimum Block Classification - MBC
Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance)
Output Ground Points, DTM
1
2
3
5
Window Size Z-threshold
32 3.2
16 1.6
8 0.8
4 0.4
2 0.2
4
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Minimum Block Classification - MBC
Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance)
Output Ground Points, DTM
Convert Terrain to TIN
Smoothing
Umbrella Filter
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Digital Surface Model
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Digital Terrain Model
21/4/2015 Dr. Ahmad Yousef
Digital Surface Model Shaded Relief
DTM Extraction
Forest Dataset …
Digital Terrain Model Shaded Relief
21/4/2015 Dr. Ahmad Yousef
45m x 35m
DTM Extraction
Urban Dataset …
5 750 000 Points
21/4/2015 Dr. Ahmad Yousef
DTM Extraction
Urban Dataset …
21/4/2015 Dr. Ahmad Yousef
3D Building Models Reconstruction
How to fulfill the gap between lidar data and building models ?
How to minimize the number of points that represent a building ?
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Buildings Shapes …
Redlands, USA. Source : Google maps
21/4/2015 Dr. Ahmad Yousef
3D Building Model
No Comments …
Venezuela. Barrio Petare, Caracas.
Source: A sustainable approach to problems in urban squatter developments
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Level of Details LoD
• Building model is the representation
used for describing the form of
building.
• The complexity of a 3D building model
is known as the level of details (LoDs).
Source: : Open Geospatial Consortium CityGML Implementation Specification 1.0,20.8 2008
Level of Details
Data Processing
LoD 1 : Flat Roofs
LoD 2 : Roof Type
LoD 3 : Real Roof Shape
LoD 4 : Interior
21/4/2015 Dr. Ahmad Yousef
Model Structure Based – Model Derive
3D Building Model
Modeling Approaches
• Model Database.
• The final roof shape is always topologically correct.
• Complex roof shapes cannot be reconstructed.
3D Building Models Database
Flat Desk Gable Hipped Mansard PyramidGambrel
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Modeling Approaches
Source: International Summer School “Digital Recording and 3D Modeling”.
3D Building Models Database
Flat Desk Gable Hipped Mansard PyramidGambrel
21/4/2015 Dr. Ahmad Yousef
Data Structure Based – Data Derive
3D Building Model
Modeling Approaches
• Roof described by planar faces.
• Partitioning the given ground plan and find the most appropriate plane
segment to each partition.
Source: International Summer School “Digital Recording and 3D Modeling”.
21/4/2015 Dr. Ahmad Yousef
Input for Data Derive Model
3D Building Model
Modeling Approaches
I. Points based
– Points may belong to several planes.
II. Raster based
– Information content is decreased due to interpolation.
III. TIN based
– To avoid loss of information due to interpolation, all operations are performed
on the Delaunay triangulation of the original height points.
– Requires more analysis.
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Building Model Elements
Planar Patches
Wall
Footprint
Planar Face
TIN Triangles
Lidar
Roof Boundary
21/4/2015 Dr. Ahmad Yousef
5 Processing Steps :-
3D Building Model
Framework …
1. Generate Triangle Irregular Network - TIN
2. Extract Roof Planar Patches
i. Normal Vector Estimation
ii. Segmentation & Region Growing
3. Detection of Planar Roof Faces
i. Least Square Plane Fitting
ii. Merging Planar Patches
4. Intersection of Roof Planes
5. 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
21/4/2015 Dr. Ahmad Yousef
3D Building Model
1 - Triangle Irregular Network
21/4/2015 Dr. Ahmad Yousef
3D Building Model
1 - Triangle Irregular Network
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
A.X + B.Y + C.Z + D = 0
N=(A,B,C)
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
1 Ring Neighborhood 2 Rings Neighborhood
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
21/4/2015 Dr. Ahmad Yousef
1 Ring Neighborhood
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
2 Rings Neighborhood
21/4/2015 Dr. Ahmad Yousef
2 Rings Neighborhood
3D Building Model
2 - Extract Roof Planar Patches
Normal Vector Estimation
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Segmentation & Region Growing
Apply region growing to find roof patches
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Segmentation & Region Growing
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Segmentation & Region Growing
21/4/2015 Dr. Ahmad Yousef
3D Building Model
2 - Extract Roof Planar Patches
Segmentation & Region Growing
21/4/2015 Dr. Ahmad Yousef
3D Building Model
3 - Detection of Planar Roof Faces
21/4/2015 Dr. Ahmad Yousef
3D Building Model
3 - Detection of Planar Roof Faces
Vertical Triangles
All triangles with slope greater than 60 are
considered as vertical patches
21/4/2015 Dr. Ahmad Yousef
3D Building Model
3 - Detection of Planar Roof Faces
Least Square Plane Fitting
A * X + B * Y + C * Z = D
The Plane Normal is given by : N = (A,B,C)
21/4/2015 Dr. Ahmad Yousef
3D Building Model
3 - Detection of Planar Roof Faces
Intersection of adjacent patches
A * X + B * Y + C * Z = D
21/4/2015 Dr. Ahmad Yousef
3D Building Model
3 - Detection of Planar Roof Faces
Merging Planar Patches
21/4/2015 Dr. Ahmad Yousef
3D Building Model
4 - Intersection of Roof Planes
Patch 1 2 3 4 5 6 7
1 YES YES YES YES
2 YES YES YES
3 YES YES
4 YES YES
5 YES YES
6 YES YES YES
7 YES
Plane Adjacency Matrix
21/4/2015 Dr. Ahmad Yousef
3D Building Model
4 - Intersection of Roof Planes
21/4/2015 Dr. Ahmad Yousef
3D Building Model
4 - Intersection of Roof Planes
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
5 - 3D Model Reconstruction
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Example 1
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Example 1
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Example 1
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Example 2
Minimum area
threshold = 5 m2
Minimum area
threshold = 3 m2
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Example 3
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Test Area
21/4/2015 Dr. Ahmad Yousef
3D Building Model
Error Sources
21/4/2015 Dr. Ahmad Yousef
Summary
Conclusion & Final Thoughts...
• Minimum Block Classification (MBC) Model was successfully implemented
in GIS environment.
• Advantages of MBC Model includes :
– Fixed number of processing loops.
– Capable of capturing and removing the major terrain features.
– Although building size and shape present a challenge for many other filtering
algorithms, they do not significantly hinder the MBC algorithm when using the
proper window size and threshold.
21/4/2015 Dr. Ahmad Yousef
Summary
Conclusion & Final Thoughts...
o Minimum Block Classification (MBC) Model was successfully implemented
in GIS environment.
o 3D building reconstruction models from lidar data was developed from
constructing a roof surface geometry.
o 3D building reconstruction models result affected by
 Minimum area threshold
 Points distribution
 Shape complexity
o The processing time varies and is dependent on the shape of the building,
start from 5 seconds for simple buildings to 30 seconds for complex
buildings.
21/4/2015 Dr. Ahmad Yousef
Thank You
Share What You Can To Benefit The Others
Ahmad Yousef (yousef@dii-eumena.com)

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Buildings3 d gThe Use of Geographical Information Systems for 3D Urban Models Reconstruction from Aerial Lidar Data

  • 1. 21/4/2015 Dr. Ahmad Yousef The Use of GIS for 3D Urban Models Reconstruction Based on Aerial Lidar Data Dr. Ahmad Yousef
  • 2. 21/4/2015 Dr. Ahmad Yousef • Overview • Objectives & Goals • Digital Terrain Model Extraction • 3D Building Model Reconstruction • Conclusion & Final Thoughts AGENDA
  • 3. 21/4/2015 Dr. Ahmad Yousef Pollution, Cairo Visualization, Dubai Pollution, Moscow Flood, Pakistan Visualization, Los Angeles City Planning, Stuttgart Flood, Mexico • GIS has traditionally been 2D technology. • New product and technology make us reconsider the role of 3D. • It is widely recognized that 3D models are necessary. Overview Why 3D GIS …
  • 4. 21/4/2015 Dr. Ahmad Yousef Year : 2050 66% Year : 2014 54% Urban Population World Source: UN DESA’s Population Division of Global Population o The overall growth of the world’s population could add another 2.5 billion people to urban populations by 2050 o 90 percent of the increase concentrated in Asia and Africa Overview Why urban area …
  • 5. 21/4/2015 Dr. Ahmad Yousef Overview Why lidar data … Waldkirch, Germany
  • 6. 21/4/2015 Dr. Ahmad Yousef Overview Workshop Objectives & Goals
  • 7. 21/4/2015 Dr. Ahmad Yousef Overview Workshop Objectives & Goals
  • 8. 21/4/2015 Dr. Ahmad Yousef Overview Workshop Objectives & Goals 1. Introduce a fast and simple integrated Digital Terrain Model (DTM) extraction framework in a GIS environment.
  • 9. 21/4/2015 Dr. Ahmad Yousef Overview Workshop Objectives & Goals 1. Introduce a fast and simple integrated Digital Terrain Model (DTM) extraction framework in a GIS environment. 2. Introduce a developed GIS approach for reconstructing of the 3D building models from lidar point clouds.
  • 10. 21/4/2015 Dr. Ahmad Yousef Digital Terrain Model Extraction How to classify lidar data into terrain and off-terrain points?
  • 11. 21/4/2015 Dr. Ahmad Yousef • Filtering means classification of points into terrain and off-terrain. • Bare earth is assumed to be continuous surface. • Filtering In/Out data: – Point list – Grid – Triangulated Irregular Network - TIN DTM Extraction Filtering lidar data
  • 12. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Esri Terrain Dataset Surface geometry at different resolution (TINs) – TIN is acronym for triangulated irregular network. Level of Detail-Pyramid LoD x Pyramid Type: I. WINDOWSIZE II. ZTOLERANCE parameter: I. ZMIN II. ZMAX III. ZMEAN IV. ZMINMAX
  • 13. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Esri Terrain Dataset
  • 14. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Esri Terrain Dataset
  • 15. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Esri Terrain Dataset
  • 16. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Esri Terrain Dataset
  • 17. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Minimum Block Classification - MBC Window Block Bare Earth Surface Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance) Output Ground Points, DTM Window Block
  • 18. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Minimum Block Classification - MBC Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance) Output Ground Points, DTM 1 2 3 5 Window Size Z-threshold 32 3.2 16 1.6 8 0.8 4 0.4 2 0.2 4
  • 19. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Minimum Block Classification - MBC Input Points Dataset [Geodatabase], Input Window Size, Cell Size (≈ Points Average Distance) Output Ground Points, DTM Convert Terrain to TIN Smoothing Umbrella Filter
  • 20. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Digital Surface Model
  • 21. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Digital Terrain Model
  • 22. 21/4/2015 Dr. Ahmad Yousef Digital Surface Model Shaded Relief DTM Extraction Forest Dataset … Digital Terrain Model Shaded Relief
  • 23. 21/4/2015 Dr. Ahmad Yousef 45m x 35m DTM Extraction Urban Dataset … 5 750 000 Points
  • 24. 21/4/2015 Dr. Ahmad Yousef DTM Extraction Urban Dataset …
  • 25. 21/4/2015 Dr. Ahmad Yousef 3D Building Models Reconstruction How to fulfill the gap between lidar data and building models ? How to minimize the number of points that represent a building ?
  • 26. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Buildings Shapes … Redlands, USA. Source : Google maps
  • 27. 21/4/2015 Dr. Ahmad Yousef 3D Building Model No Comments … Venezuela. Barrio Petare, Caracas. Source: A sustainable approach to problems in urban squatter developments
  • 28. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Level of Details LoD • Building model is the representation used for describing the form of building. • The complexity of a 3D building model is known as the level of details (LoDs). Source: : Open Geospatial Consortium CityGML Implementation Specification 1.0,20.8 2008 Level of Details Data Processing LoD 1 : Flat Roofs LoD 2 : Roof Type LoD 3 : Real Roof Shape LoD 4 : Interior
  • 29. 21/4/2015 Dr. Ahmad Yousef Model Structure Based – Model Derive 3D Building Model Modeling Approaches • Model Database. • The final roof shape is always topologically correct. • Complex roof shapes cannot be reconstructed. 3D Building Models Database Flat Desk Gable Hipped Mansard PyramidGambrel
  • 30. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Modeling Approaches Source: International Summer School “Digital Recording and 3D Modeling”. 3D Building Models Database Flat Desk Gable Hipped Mansard PyramidGambrel
  • 31. 21/4/2015 Dr. Ahmad Yousef Data Structure Based – Data Derive 3D Building Model Modeling Approaches • Roof described by planar faces. • Partitioning the given ground plan and find the most appropriate plane segment to each partition. Source: International Summer School “Digital Recording and 3D Modeling”.
  • 32. 21/4/2015 Dr. Ahmad Yousef Input for Data Derive Model 3D Building Model Modeling Approaches I. Points based – Points may belong to several planes. II. Raster based – Information content is decreased due to interpolation. III. TIN based – To avoid loss of information due to interpolation, all operations are performed on the Delaunay triangulation of the original height points. – Requires more analysis.
  • 33. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Building Model Elements Planar Patches Wall Footprint Planar Face TIN Triangles Lidar Roof Boundary
  • 34. 21/4/2015 Dr. Ahmad Yousef 5 Processing Steps :- 3D Building Model Framework … 1. Generate Triangle Irregular Network - TIN 2. Extract Roof Planar Patches i. Normal Vector Estimation ii. Segmentation & Region Growing 3. Detection of Planar Roof Faces i. Least Square Plane Fitting ii. Merging Planar Patches 4. Intersection of Roof Planes 5. 3D Model Reconstruction
  • 36. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 1 - Triangle Irregular Network
  • 37. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 1 - Triangle Irregular Network
  • 38. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches A.X + B.Y + C.Z + D = 0 N=(A,B,C)
  • 39. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation 1 Ring Neighborhood 2 Rings Neighborhood
  • 40. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation
  • 41. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation
  • 42. 21/4/2015 Dr. Ahmad Yousef 1 Ring Neighborhood 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation
  • 43. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation 2 Rings Neighborhood
  • 44. 21/4/2015 Dr. Ahmad Yousef 2 Rings Neighborhood 3D Building Model 2 - Extract Roof Planar Patches Normal Vector Estimation
  • 45. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Segmentation & Region Growing Apply region growing to find roof patches
  • 46. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Segmentation & Region Growing
  • 47. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Segmentation & Region Growing
  • 48. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 2 - Extract Roof Planar Patches Segmentation & Region Growing
  • 49. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 3 - Detection of Planar Roof Faces
  • 50. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 3 - Detection of Planar Roof Faces Vertical Triangles All triangles with slope greater than 60 are considered as vertical patches
  • 51. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 3 - Detection of Planar Roof Faces Least Square Plane Fitting A * X + B * Y + C * Z = D The Plane Normal is given by : N = (A,B,C)
  • 52. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 3 - Detection of Planar Roof Faces Intersection of adjacent patches A * X + B * Y + C * Z = D
  • 53. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 3 - Detection of Planar Roof Faces Merging Planar Patches
  • 54. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 4 - Intersection of Roof Planes Patch 1 2 3 4 5 6 7 1 YES YES YES YES 2 YES YES YES 3 YES YES 4 YES YES 5 YES YES 6 YES YES YES 7 YES Plane Adjacency Matrix
  • 55. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 4 - Intersection of Roof Planes
  • 56. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 4 - Intersection of Roof Planes
  • 57. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 58. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 59. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 60. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 61. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 62. 21/4/2015 Dr. Ahmad Yousef 3D Building Model 5 - 3D Model Reconstruction
  • 63. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Example 1
  • 64. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Example 1
  • 65. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Example 1
  • 66. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Example 2 Minimum area threshold = 5 m2 Minimum area threshold = 3 m2
  • 67. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Example 3
  • 68. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Test Area
  • 69. 21/4/2015 Dr. Ahmad Yousef 3D Building Model Error Sources
  • 70. 21/4/2015 Dr. Ahmad Yousef Summary Conclusion & Final Thoughts... • Minimum Block Classification (MBC) Model was successfully implemented in GIS environment. • Advantages of MBC Model includes : – Fixed number of processing loops. – Capable of capturing and removing the major terrain features. – Although building size and shape present a challenge for many other filtering algorithms, they do not significantly hinder the MBC algorithm when using the proper window size and threshold.
  • 71. 21/4/2015 Dr. Ahmad Yousef Summary Conclusion & Final Thoughts... o Minimum Block Classification (MBC) Model was successfully implemented in GIS environment. o 3D building reconstruction models from lidar data was developed from constructing a roof surface geometry. o 3D building reconstruction models result affected by  Minimum area threshold  Points distribution  Shape complexity o The processing time varies and is dependent on the shape of the building, start from 5 seconds for simple buildings to 30 seconds for complex buildings.
  • 72. 21/4/2015 Dr. Ahmad Yousef Thank You Share What You Can To Benefit The Others Ahmad Yousef (yousef@dii-eumena.com)