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A platform for Spatial Data Labelling in a UrbanContext
Julien Lesbegueries, Nicolas Lachiche, Agnes Braud, AnnePuissant, Grzegorz Skupinski and Julien Perret
FDBT-LSIIT LIVE, Strasbourg and COGIT, IGN
11 mai 2009
The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Context
Urban classification context
Aims at classifying areas of a city from topologic map
In order to evaluate several potential evolutions for the city
Specific urban surface
Continuous fabric
Urban fabric withindividual houses
Urban fabric withcollective buildings
Mixed housing surface
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Context
Urban classification context
Aims at classifying areas of a city from topologic map
In order to evaluate several potential evolutions for the city
1956 1966 1976
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Plan
1 The platform global schema
2 Defining the Labelling ProcedureModelling the problemData processing
3 Uses of the platform
4 Conclusion : Geoxygene Plug-in for semi-automatic labelling
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Platform Board
Sliders for gradated labellings
Add-commentfunction
Visualizedlayers
Area to belabelled
A B
D
C
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Plan
1 The platform global schema
2 Defining the Labelling ProcedureModelling the problemData processing
3 Uses of the platform
4 Conclusion : Geoxygene Plug-in for semi-automatic labelling
The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
From a specific urban problem to a generic procedure
Our problem is a typical data mining problem
Our procedure / module aims at being adaptable
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Modelling the problem
Modelling the problem
Input Data
Geographic layers (vector-basedtopographic)
Target layer to be labelled
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
Roads
Vegetation
Buildings
Areas
y
x
Layers
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Modelling the problem
Labels definition
Sliders / Combo Box generation
Iterative procedure
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
1 Continuous urban fabric (city center),
2 Discontinuous urban fabric with individual houses,
3 Discontinuous urban fabric with collective buildings,
4 High density mixed housing surface (mix of 2 and 3),
5 ...
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Modelling the problem
Procedure definition
Binary labelling
Labelling with a confidence level
Overlapping classes/labels concept
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
Mixed housing ...Specific urban ...Urban fabric ...
0 4
(a) Binary labelling (b) Confidence level or Overlapping classes
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Modelling the problem
Consensus validation
Tools to evaluate the labelling process
Tools to evaluate the consensus
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Data processing
Performing the labelling
Random area to label
Area to label chosen by the user
Several scales of visualization
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Data processing
Learning a model
Supervised learning (Decision tree,SVM, Collective learning, . . .)
Decision tree (and rules-based models)offer readable models
Relational approaches (structure ofgeographic data) to be included
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
Example
1.maxairebat <= 284.319075: hdtpi (40.0)
2.
3.densite <= 0.280899 AND
4.medairebat > 355.372301 AND
5.maxairebat <= 2015.45846: hdtcge (36.0/2.0)
6.
7.densite <= 0.272948 AND
8.medairebat <= 449.669789 AND
9.maxairebat <= 1367.026327 AND
10.moyconvbat > 0.935598: hdmpd (35.0/2.0)
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Data processing
Performing the automatic labelling
Using the learned model
Global validation (or not)
Vector based geographic layers(among which the target one)
Labels definition Proceduredefinition
Consensusvalidation
Performingthe labelling
Learninga model
Performing theautomaticlabelling
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Possible uses of the platform
Labelling urban areas
A model must be learned for each city
Categorize kinds of cities to apply existing models ?
Other labelling processes
Generic module
Need of vector layers, a target one and a list of labels as input
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The platform global schema Defining the Labelling Procedure Uses of the platform Conclusion
Semi-automatic labelling plug-in
Conclusion
This module allowed us to find a model to label an entire city
It showed that the model was not generalizable to other cities
Further works
Integration to Geoxygene (in progress)
Integrate more specific techniques (relational learning, activelearning, semi-supervised learning)
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A platform for Spatial Data Labelling in a UrbanContext
Julien Lesbegueries, Nicolas Lachiche, Agnes Braud, AnnePuissant, Grzegorz Skupinski and Julien Perret
FDBT-LSIIT LIVE, Strasbourg and COGIT, IGN
11 mai 2009
Merci
