ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations...
description
Transcript of 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
23.08.13 2
ICC 2013 Dresden
MULTI-SCALES GENERALISATION
Definition:
Generalise maps at any scale from multiple sources
23.08.13 3
ICC 2013 Dresden
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
MULTI-SCALES GENERALISATION
Problem similar to « continuous » generalisation (Harrie et al 2002, van Oosterom 2005)
23.08.13 4
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
MULTI-SCALES GENERALISATION
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.)
23.08.13 5
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
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
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 7
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 8
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 9
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 10
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 11
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 12
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
e.g. roundabouts or braids in rivers
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 13
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
e.g. select roads whose « number of lanes » is > 2
FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
23.08.13 14
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
e.g. Gaussian smoothing [step = 5 m]
or strokes selection [minLength > 500 m, T number > 4]
FROM SCALEMASTER TO SCALEMASTER2.0
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)
23.08.13 15
ICC 2013 Dresden
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
FROM SCALEMASTER TO SCALEMASTER2.0
Automatic process of the ScaleMaster
23.08.13 16
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
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)
23.08.13 17
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
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.)
23.08.13 18
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
RESULTS
23.08.13 19
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
Tests on real data with 3 sources:
VMAP2 (~1:50K)
VMAP1 (~1:250K)
VMAP0 (~1:1000K)
Roads
RESULTS
23.08.13 20
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
Tests on real data with 3 sources:
VMAP2 (~1:50K)
VMAP1 (~1:250K)
VMAP0 (~1:1000K)
River simplification
RESULTS
23.08.13 21
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
Tests on real data with 3 sources:
VMAP2 (~1:50K)
VMAP1 (~1:250K)
VMAP0 (~1:1000K)
Land use generalisation
RESULTS
23.08.13 22
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
Tests on real data with 3 sources:
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
23.08.13 23
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
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
23.08.13 24
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
FURTHER WORK
23.08.13 25
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
THANKS FOR YOUR ATTENTION
ANY QUESTIONS? 26
COGIT – IGN France
Guillaume Touya & Jean-François Girres
http://oxygene-project.sourceforge.net/
RESULTS
23.08.13 27
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
Tests on real data with 3 sources:
VMAP2 (~1:50K)
VMAP1 (~1:250K)
VMAP0 (~1:1000K)
River selection with strokes
IMPLEMENTED ALGORITHMS
23.08.13
ICC 2013 Dresden
28
Line simplification (Raposo 2010)
Concave cover for points
IMPLEMENTED ALGORITHMS
23.08.13
ICC 2013 Dresden
29
Visvalingam-Whyatt on coastlines
Railway line generalisation
FROM SCALEMASTER TO SCALEMASTER2.0
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 )
23.08.13 30
ICC 2013 Dresden
Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
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