A platform for Spatial Data Labelling in a Urban Context

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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

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