3 D Lidar Epfl Iccsa 08


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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

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3 D Lidar Epfl Iccsa 08

  1. 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. 2. Context of the research <ul><li>Mega cities dynamics = complex auto-organised systems </li></ul><ul><li>Goal = develop simulation platform for decision support </li></ul><ul><li>=> assess scenarios of impacts of new architectural programs </li></ul><ul><li>Features (cadastre + SwissTopo datas): </li></ul><ul><ul><li>Buildings + their programmatic use </li></ul></ul><ul><ul><li>Built environment : roads, railway, bus </li></ul></ul><ul><ul><li>Natural environment: rivers, lake, green areas </li></ul></ul><ul><ul><li>Administrative limits </li></ul></ul><ul><li>+ LIDAR data => construct accurate 3D urban surface model for visibility analysis </li></ul>
  3. 3. Approach: Vector Multi-scale Multi-agent geosimulation <ul><li>Cognitive agents </li></ul><ul><ul><li>buildings </li></ul></ul><ul><ul><li>equipments </li></ul></ul><ul><ul><li>public places </li></ul></ul><ul><ul><li>residential groups </li></ul></ul><ul><ul><li>urban systems </li></ul></ul><ul><ul><li>Towns </li></ul></ul><ul><li>Objects of the environment </li></ul><ul><ul><li>Roads </li></ul></ul><ul><ul><li>Lakes </li></ul></ul><ul><ul><li>Rivers </li></ul></ul><ul><ul><li>Green areas </li></ul></ul><ul><ul><li>Railways and stations </li></ul></ul><ul><ul><li>Public transports </li></ul></ul>BUILDINGS Residential Equipment PUBLIC PLACES Street Place Park M I CRO M ESO M A CRO RESIDENTIAL GROUP URBAN SYSTEM TOWN MEGA CITY
  4. 4. Laws <ul><li>Agents have different behaviours according to programmatic use and environment </li></ul><ul><li>5 kinds of laws : </li></ul><ul><ul><li>Growing : probabilistic residential and derived equipment growing rate </li></ul></ul><ul><ul><li>Stability : probabilistic end of life according to predefined thresholds </li></ul></ul><ul><ul><li>Influence : programmatic influence between programs (services / injures) </li></ul></ul><ul><ul><li>Morphology : groups’ optimization </li></ul></ul><ul><ul><li>Physical constraints : neighbourhood thresholds, slope </li></ul></ul><ul><li>Visibility on particular attractions (e.g. lake Geneva) </li></ul><ul><li>Sunshine exposure </li></ul>
  5. 5. I am a new villa, I want to be close to school and have view to lake SCHOOL SCHOOL Possible locations Here !
  6. 6. Satisfaction degree of an agent DS = Weighted average of satisfaction criteria
  7. 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. 8. Visibility analysis: 2 perspectives <ul><li>Using directly 3D urban surface model, from top and ground surfaces </li></ul><ul><li>Over buildings façades, in a vertical direction (elevation) </li></ul>Grid T(i,j) Resolution: 1m/1m Visibility = ∑ i,j T(i,j) Top down
  9. 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. 10. Results Simulation without visibility parameter Simulation with visibility parameter Lake Geneva Lake Geneva Lake Geneva
  11. 11. Conclusions <ul><li>Conception of the approach, first results </li></ul><ul><li>Visibility indicators have great importance for localization => precision of LIDAR data particularly interesting </li></ul><ul><li>Improvements on the simulations results => better interpretation of importance of LIDAR data </li></ul><ul><li>Time consuming </li></ul><ul><li>Great perspectives … </li></ul><ul><li>Implement the approach into the system would allow: </li></ul><ul><ul><li>Integrate visibilty and sunshine analysis at each transition </li></ul></ul><ul><ul><li>Dynamic visualisation </li></ul></ul><ul><ul><li>Compute potential visibility for new buildings </li></ul></ul>
  12. 12. Implementation Shp files LIDAR data -> TerraSan Programming -> MATLAB Spatial Analysis -> Manifold GIS Visibility and sunshine analysis
  13. 13. Further 3D Lidar data applications <ul><li>Compute solar exposure (8 orientations) </li></ul><ul><li>Morphological indicators </li></ul><ul><ul><li>Global 3D volume (m3) </li></ul></ul><ul><ul><li>3D complexity: number of faces </li></ul></ul><ul><ul><li>Roof slope => classification </li></ul></ul><ul><li>Dynamic 3D interface for more realistic view of city morphogenesis </li></ul>2,5D 3D => More accurate volume
  14. 14. Thank you for your attention !
  15. 15. Cognitive agents Objects of the environment UML diagram
  16. 16. Satisfaction degree (influences)
  17. 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. 18. Construction of 3D urban surface model
  19. 19. Visibility analysis from top and ground surfaces
  20. 20. Visibility analysis from side surfaces
  21. 21. … from side surfaces Evaluate the potential height of a future new building …