Presentation at the International Cartographic Conference (ICC'13 Dresden) of the paper: "ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations" by G. Touya and J.F. Girres

1. SCALEMASTER2.0: A SCALEMASTER EXTENSION TO MONITOR AUTOMATIC MULTI-SCALES GENERALISATIONS
Guillaume Touya & Jean-François Girres
ICC 2013 Dresden
COGIT lab – IGN France

2. OUTLINE
Multi-Scales Generalisation
From ScaleMaster to ScaleMaster2.0
Implementation with algorithms
Results
Conclusion and Further Work
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3. MULTI-SCALES GENERALISATION
Definition:
Generalise maps at any scale from multiple sources
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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

4. MULTI-SCALES GENERALISATION
Problem similar to « continuous » generalisation (Harrie et al 2002, van Oosterom 2005)
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

5. 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.)
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

6. 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
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

7. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

8. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

9. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

10. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

11. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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ICC 2013 Dresden
Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

12. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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ICC 2013 Dresden Multi-scales generalisation ScaleMaster2.0 Implementation Results Conclusion
e.g. roundabouts or braids in rivers

13. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion
e.g. select roads whose « number of lanes » is > 2

14. FROM SCALEMASTER TO SCALEMASTER2.0
The ScaleMaster2.0
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion
e.g. Gaussian smoothing [step = 5 m]
or strokes selection [minLength > 500 m, T number > 4]

15. 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)
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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

16. FROM SCALEMASTER TO SCALEMASTER2.0
Automatic process of the ScaleMaster
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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

17. IMPLEMENTATION
Implemented in CartAGen open source generalisation platform (Renard et al 2010)
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion
MRDB
DLM 1
DLM 2
DLM 3
DCM
ScaleMaster2.0
CartAGen
ScaleMaster.xml
Parameters. xml
Ontologies

18. 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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ICC 2013
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

19. RESULTS
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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

20. RESULTS
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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

21. RESULTS
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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

22. RESULTS
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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

23. 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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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

24. 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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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

25. FURTHER WORK
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Multi-scales generalisation
ScaleMaster2.0
Implementation
Results
Conclusion

26. THANKS FOR YOUR ATTENTION ANY QUESTIONS?
26
COGIT – IGN France
Guillaume Touya & Jean-François Girres
http://oxygene-project.sourceforge.net/

27. RESULTS
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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

28. IMPLEMENTED ALGORITHMS
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Line simplification (Raposo 2010)
Concave cover for points

29. IMPLEMENTED ALGORITHMS
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Visvalingam-Whyatt on coastlines
Railway line generalisation

30. 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 )
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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