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3 D Lidar Epfl Iccsa 08

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The document discusses using 3D LIDAR data and multi-agent geosimulation to model urban morphogenesis and assess the impacts of new development scenarios. Key aspects include developing an accurate 3D surface model from LIDAR and cadaster data to analyze visibility and sunlight exposure. Agents representing buildings and urban elements interact based on programmed behaviors and environmental factors. Preliminary results show visibility is an important parameter and LIDAR data provides precision, though computations are time-consuming. Further applications of high-resolution 3D LIDAR data could improve simulation accuracy and dynamic visualization.

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3D LIDAR data application for urban morphogenesis multi-agent vector based geosimulation Vitor Silva, Dr. Corinne Plazanet, Cláudio Carneiro, Pr. François Golay 2008 ICCSA conference GEOG-AN-MOD session 2008 July the 2 nd
Context of the research Mega cities dynamics = complex auto-organised systems Goal = develop simulation platform for decision support => assess scenarios of impacts of new architectural programs Features (cadastre + SwissTopo datas): Buildings + their programmatic use Built environment : roads, railway, bus Natural environment: rivers, lake, green areas Administrative limits + LIDAR data => construct accurate 3D urban surface model for visibility analysis
Approach: Vector Multi-scale Multi-agent geosimulation Cognitive agents buildings equipments public places residential groups urban systems Towns Objects of the environment Roads Lakes Rivers Green areas Railways and stations Public transports BUILDINGS Residential Equipment PUBLIC PLACES Street Place Park M I CRO M ESO M A CRO RESIDENTIAL GROUP URBAN SYSTEM TOWN MEGA CITY
Laws Agents have different behaviours according to programmatic use and environment 5 kinds of laws : Growing : probabilistic residential and derived equipment growing rate Stability : probabilistic end of life according to predefined thresholds Influence : programmatic influence between programs (services / injures) Morphology : groups’ optimization Physical constraints : neighbourhood thresholds, slope Visibility on particular attractions (e.g. lake Geneva) Sunshine exposure
I am a new villa, I want to be close to school and have view to lake SCHOOL SCHOOL Possible locations Here !
Satisfaction degree of an agent DS = Weighted average of satisfaction criteria
Construction of 3D urban surface model 3D urban surface model LIDAR data , 6-8 pts / m2 Vertical resolution: 15 cm, Horizontal resolution: 20 cm Normalised data height = terrain – building elevation Cadastre (vectorial building footprints) D.T.M. Building model LIDAR roof coverage good insufficient
Visibility analysis: 2 perspectives Using directly 3D urban surface model, from top and ground surfaces Over buildings façades, in a vertical direction (elevation) Grid T(i,j) Resolution: 1m/1m Visibility = ∑ i,j T(i,j) Top down
Example of visibility analysis ~ 12 kms ~ 2,5 kms Constrained to narrow angle, due to heavy time consuming computation For each point, 2-5 minutes computation (3’000’000 pixels) Pilot area: 500 × 500 meters
Results Simulation without visibility parameter Simulation with visibility parameter Lake Geneva Lake Geneva Lake Geneva
Conclusions Conception of the approach, first results Visibility indicators have great importance for localization => precision of LIDAR data particularly interesting Improvements on the simulations results => better interpretation of importance of LIDAR data Time consuming Great perspectives … Implement the approach into the system would allow: Integrate visibilty and sunshine analysis at each transition Dynamic visualisation Compute potential visibility for new buildings
Implementation Shp files LIDAR data -> TerraSan Programming -> MATLAB Spatial Analysis -> Manifold GIS Visibility and sunshine analysis
Further 3D Lidar data applications Compute solar exposure (8 orientations) Morphological indicators Global 3D volume (m3) 3D complexity: number of faces Roof slope => classification Dynamic 3D interface for more realistic view of city morphogenesis 2,5D 3D => More accurate volume
Thank you for your attention !
Cognitive agents Objects of the environment UML diagram
Satisfaction degree (influences)
Level of Detail (LOD) definition for 3D city models [aus: Gröger, Kolbe & al.] More resolution, details cadastre, airborne LiDAR laser / LiDAR airborne LiDAR, photogrammetry Photogrammetry and ground LiDAR (building façades), hybrid methods ground LiDAR LIDAR: airborne LIght Detection And Ranging
Construction of 3D urban surface model
Visibility analysis from top and ground surfaces
Visibility analysis from side surfaces
… from side surfaces Evaluate the potential height of a future new building …

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3 D Lidar Epfl Iccsa 08

  • 1. 3D LIDAR data application for urban morphogenesis multi-agent vector based geosimulation Vitor Silva, Dr. Corinne Plazanet, Cláudio Carneiro, Pr. François Golay 2008 ICCSA conference GEOG-AN-MOD session 2008 July the 2 nd
  • 2. Context of the research Mega cities dynamics = complex auto-organised systems Goal = develop simulation platform for decision support => assess scenarios of impacts of new architectural programs Features (cadastre + SwissTopo datas): Buildings + their programmatic use Built environment : roads, railway, bus Natural environment: rivers, lake, green areas Administrative limits + LIDAR data => construct accurate 3D urban surface model for visibility analysis
  • 3. Approach: Vector Multi-scale Multi-agent geosimulation Cognitive agents buildings equipments public places residential groups urban systems Towns Objects of the environment Roads Lakes Rivers Green areas Railways and stations Public transports BUILDINGS Residential Equipment PUBLIC PLACES Street Place Park M I CRO M ESO M A CRO RESIDENTIAL GROUP URBAN SYSTEM TOWN MEGA CITY
  • 4. Laws Agents have different behaviours according to programmatic use and environment 5 kinds of laws : Growing : probabilistic residential and derived equipment growing rate Stability : probabilistic end of life according to predefined thresholds Influence : programmatic influence between programs (services / injures) Morphology : groups’ optimization Physical constraints : neighbourhood thresholds, slope Visibility on particular attractions (e.g. lake Geneva) Sunshine exposure
  • 5. I am a new villa, I want to be close to school and have view to lake SCHOOL SCHOOL Possible locations Here !
  • 6. Satisfaction degree of an agent DS = Weighted average of satisfaction criteria
  • 7. Construction of 3D urban surface model 3D urban surface model LIDAR data , 6-8 pts / m2 Vertical resolution: 15 cm, Horizontal resolution: 20 cm Normalised data height = terrain – building elevation Cadastre (vectorial building footprints) D.T.M. Building model LIDAR roof coverage good insufficient
  • 8. Visibility analysis: 2 perspectives Using directly 3D urban surface model, from top and ground surfaces Over buildings façades, in a vertical direction (elevation) Grid T(i,j) Resolution: 1m/1m Visibility = ∑ i,j T(i,j) Top down
  • 9. Example of visibility analysis ~ 12 kms ~ 2,5 kms Constrained to narrow angle, due to heavy time consuming computation For each point, 2-5 minutes computation (3’000’000 pixels) Pilot area: 500 × 500 meters
  • 10. Results Simulation without visibility parameter Simulation with visibility parameter Lake Geneva Lake Geneva Lake Geneva
  • 11. Conclusions Conception of the approach, first results Visibility indicators have great importance for localization => precision of LIDAR data particularly interesting Improvements on the simulations results => better interpretation of importance of LIDAR data Time consuming Great perspectives … Implement the approach into the system would allow: Integrate visibilty and sunshine analysis at each transition Dynamic visualisation Compute potential visibility for new buildings
  • 12. Implementation Shp files LIDAR data -> TerraSan Programming -> MATLAB Spatial Analysis -> Manifold GIS Visibility and sunshine analysis
  • 13. Further 3D Lidar data applications Compute solar exposure (8 orientations) Morphological indicators Global 3D volume (m3) 3D complexity: number of faces Roof slope => classification Dynamic 3D interface for more realistic view of city morphogenesis 2,5D 3D => More accurate volume
  • 14. Thank you for your attention !
  • 15. Cognitive agents Objects of the environment UML diagram
  • 17. Level of Detail (LOD) definition for 3D city models [aus: Gröger, Kolbe & al.] More resolution, details cadastre, airborne LiDAR laser / LiDAR airborne LiDAR, photogrammetry Photogrammetry and ground LiDAR (building façades), hybrid methods ground LiDAR LIDAR: airborne LIght Detection And Ranging
  • 18. Construction of 3D urban surface model
  • 19. Visibility analysis from top and ground surfaces
  • 20. Visibility analysis from side surfaces
  • 21. … from side surfaces Evaluate the potential height of a future new building …