scalemaster 2.0: a scalemaster extension to monitor automatic multi-scales generalizations...

SCALEMASTER2.0: A SCALEMASTER

EXTENSION TO MONITOR AUTOMATIC

MULTI-SCALES GENERALISATIONS

Guillaume Touya & Jean-François Girres

ICC 2013 Dresden

COGIT lab – IGN France

OUTLINE

Multi-Scales Generalisation

From ScaleMaster to ScaleMaster2.0

Implementation with algorithms

Results

Conclusion and Further Work

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MULTI-SCALES GENERALISATION

Definition:

Generalise maps at any scale from multiple sources

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Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion

MRDB

1:250k

1:100k

1:50k

Landuse 1:50k

Topography 1:25k

1:75k map

Touristic sites

1:75k touristic

map

With a multiple representations database

With unrelated databases


Problem similar to « continuous » generalisation (Harrie et al 2002, van Oosterom 2005)

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Problem similar to « continuous » generalisation (Harrie et al 2002, van Oosterom 2005)

ScaleMaster (Brewer & Buttenfield 2007) even more adapted

Maps derivable for any scale in the « scale line »

Multiple sources allowed

Multiple map types allowed (e.g. topographic, touristic, road map, etc.)

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FROM SCALEMASTER TO SCALEMASTER2.0

How to automate the ScaleMaster?

Replace manual operation by parameterised automatic processes

Create a formal model of the ScaleMaster

Deal with key generalisation issues

Enrichment

Priorities

Schema transformation

Multi-themes processes 23.08.13 6

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The ScaleMaster2.0

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The ScaleMaster2.0

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The ScaleMaster2.0

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The ScaleMaster2.0

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The ScaleMaster2.0

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The ScaleMaster2.0

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e.g. roundabouts or braids in rivers


The ScaleMaster2.0

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e.g. select roads whose « number of lanes » is > 2


The ScaleMaster2.0

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e.g. Gaussian smoothing [step = 5 m]

or strokes selection [minLength > 500 m, T number > 4]


Ontologies to control interoperability

Ontology of geographic concepts (e.g. river, road, forest, etc.)

Ontology of algorithms (Gould & Chaudhry 2012 )

Ontology of generalisation operators (Touya et al 2010)

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Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion Generalisation

operation

harmonisation

typification enhancement

simplification

displacement filtering

caricature

collapse

isA isA

aggregation

isA

isA

smoothing selection

isA

collapse aggregation


Automatic process of the ScaleMaster

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Get a theme

scale

Get element

from scale

Order processes

Parameter process

Execute process

Processes left?

yes

no

IMPLEMENTATION

Implemented in CartAGen open source generalisation platform (Renard et al 2010)

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MRDB

DLM 1

DLM 2

DLM 3

DCM ScaleMaster

2.0 CartAGen

ScaleMaster.xml

Parameters.xml

Ontologies

IMPLEMENTATION

A toolbox of geometric algorithms:

Line simplification (Visvalingam & Whyatt 94, Raposo 2010)

Points cloud typification

Polygon simplification (Ruas 88)

Skeletonisation (Haunert 2004) …

A toolbox of contextual processes:

Road and river strokes selection (Thomson & Richardson 99)

Airport features generalisation, …

A toolbox of correction processes (river flow, planar networks, etc.)

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RESULTS

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Tests on real data with 3 sources:

VMAP2 (~1:50K)

VMAP1 (~1:250K)

VMAP0 (~1:1000K)

Roads

RESULTS

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VMAP2 (~1:50K)

VMAP1 (~1:250K)

VMAP0 (~1:1000K)

River simplification

RESULTS

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VMAP2 (~1:50K)

VMAP1 (~1:250K)

VMAP0 (~1:1000K)

Land use generalisation

RESULTS

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VMAP2 (~1:50K)

VMAP1 (~1:250K)

VMAP0 (~1:1000K)

Airports

CONCLUSION

ScaleMaster is adapted to multi-scales generalisation

ScaleMaster2.0 is an automatic version of the ScaleMaster

Implementation on Open Source Platform

Results prove the usability with complex processes

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FURTHER WORK

Extend to symbolisation with SLD standard

Extend to partitioning techniques to process very large datasets

Extend to landscape-oriented parameterisation

Introduce some kind of constraint parameterisation

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FURTHER WORK

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THANKS FOR YOUR ATTENTION

ANY QUESTIONS? 26

COGIT – IGN France

Guillaume Touya & Jean-François Girres

http://oxygene-project.sourceforge.net/

RESULTS

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VMAP2 (~1:50K)

VMAP1 (~1:250K)

VMAP0 (~1:1000K)

River selection with strokes

IMPLEMENTED ALGORITHMS

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Line simplification (Raposo 2010)

Concave cover for points

IMPLEMENTED ALGORITHMS

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Visvalingam-Whyatt on coastlines

Railway line generalisation


Ontologies to control interoperability Ontology of geographic concepts (e.g. river, road,

forest, etc.) Ontology of generalisation operators (Touya et al 2010)

Ontology of algorithms and processes (Gould &

Chaudhry 2012 )

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Algorithm Process

Operation

implements triggers

triggers

Geographic entity

appliesTo

appliesTo

Parameter

hasParameter

hasParameter

Geometry

line point

polygon

isA

isA isA

geometryType geometryType

road

building

isA

isA

Douglas & Peucker

S.O.M. Typification AGENT

Least squares

Ontology individual

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