3D LIDAR data application 3D LIDAR data application

for urban morphogenesis for urban morphogenesis

multi-agent vector based geosimulationmulti-agent vector based geosimulation

Vitor Silva, Dr. Corinne Plazanet, Cláudio Carneiro, Pr. François Golay

2008 ICCSA conferenceGEOG-AN-MOD session

2008 July the 2nd

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

MICRO

MESO

MACRO

RESIDENTIAL GROUP URBAN SYSTEM

TOWN

MEGA CITY

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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

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I am a new villa, I want to be close to school and have view to lake

School

SCHOOLLake

School

SCHOOLLake

Possible locations

Here !

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Satisfaction degree of an agent

Visibility Satisfaction

Degree

For each agent a

n

ii

n

iii

solCoefvisCoefaaluenceCoef

solCoefsaDSvisCoefvaDSaaluenceCoefaaDSaDS

1

1

__),(inf_

_*),(_*),(),(inf_*),()(

[%]),( vaDS

Solar exposition satisfaction

degree

[%]),( saDS

For each influencing function f

If « asking » relation beetween a and f

Type_influence

If positive influence

If negative influence

Search for the closest

influencing agent/object ai

)),(_

)(),(1(*100),(

aiaInfluenceMax

aiaiadaiaDS

If « carrying » relation beetween a and f

Search for all influencing

agent/object aij

n

j ij

ijij

aaInfluenceMax

aaad

naiaDS

1

)),(_

)(),(1(100

1),(

Search for all influencing

agent/object aij

)),(_

)(),((*100),(

aiaInfluenceMax

aiaiadaiaDS

DS = Weighted average of satisfaction criteria

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3D urban surface model

Construction of 3D urban surface modelLIDAR 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

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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 d

ow

n

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Example of visibility analysis~

12 k

ms

~ 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

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Results

Simulation without visibility parameter

Simulation with visibility parameter

Lake Geneva

Lake GenevaLake Geneva

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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

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Implementation

PostGISVisualisation

GOOGLE EARTH

Données source (SwissTopo,

cadastre, orthophotos)

GeoSimulateur

ExportKML

Packages JAVA

GeOxygène

import

Environnement de développement Eclipse

MAPPING

LIDAR data -> TerraSanProgramming -> MATLAB Spatial Analysis -> Manifold GIS

Visibility and sunshine analysis

Shp files

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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

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Thank you for your attention !

15

Cognitive agents

Objects of the environment

UML diagram

16

Satisfaction degreeFor each agent a

For each influencing function f

If request from a to f

Type_influenceIf positive influence

If negative influence

Search for the closest influencing

agent/object ai

)),(_

)(),(1(*100),(

aiaInfluenceMax

aiaiadaiaDS

If service from f to a

Search for all

influencing agent/object

aij

n

j ij

ijij

aaInfluenceMax

aaad

naiaDS

1

)),(_

)(),(1(100

1),(

Search for all

influencing agent/object

aij

)),(_

)(),((*100),(

aiaInfluenceMax

aiaiadaiaDS

0

n

ii

n

iii

solCoefvisCoefaaluenceCoef

solCoefsaDSvisCoefvaDSaaluenceCoefaaDSaDS

1

1

__),(inf_

_*),(_*),(),(inf_*),()(

(influences)

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Level of Detail (LOD) definition for 3D city models

[aus: Gröger, Kolbe & al.]

Mo

re resolu

tion

, 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

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Construction of 3D urban surface model

19

Visibility analysis from top and ground surfaces

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Visibility analysis from side surfaces

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… from side surfacesEvaluate the potential height of a future new building …