Here are the slide of my thesis defense on 18 january 2023 entitled: "Exploring the potential of deep learning for map generalization"

1. Exploring the potential of
deep learning for map
generalization
Azelle Courtial
Supervised by Guillaume Touya and Xiang Zhang

2. Context : map generalization
Exploring the potential of deep learning for map generalization
2

3. Context : map generalization
Exploring the potential of deep learning for map generalization
3
Operators
Selection
Enlargement
Simplification
Typification
Amalgamation

4. Context : map generalization
Exploring the potential of deep learning for map generalization
4
Geometry,
Attribute,
Context,
Etc.
Multi criteria
decision

5. Context : map generalization
Exploring the potential of deep learning for map generalization
5

6. Context : base idea
Exploring the potential of deep learning for map generalization
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(Simo-serra et al. 2017)​

7. Context : deep learning principle
Exploring the potential of deep learning for map generalization
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NOT
A
MAP
A
MAP
A
MAP
Input
Prediction
Target
Model

8. Context : deep learning principle
Exploring the potential of deep learning for map generalization
8
1. Model
2. Training
3. Dataset
4. Loss function
Issues

9. Issue 1. Designing an adapted deep
neural network
Exploring the potential of deep learning for map generalization
9

10. Issue 2. Training efficiently the model
Exploring the potential of deep learning for map generalization
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11. Issue 3. Designing an adapted
training set
Exploring the potential of deep learning for map generalization
11
Input Target

12. Issue 4. Measuring the quality of
a prediction
Exploring the potential of deep learning for map generalization
12
Could you guess which prediction is the best?

13. Objectives
Exploring the potential of deep learning for map generalization
13
Explore the potential of deep learning to
contribute to map generalization research.

14. Method : an exploration through three use
cases
Exploring the potential of deep learning for map generalization
14

15. Method : an exploration through three use
cases
Exploring the potential of deep learning for map generalization
15
Generalizing mountain road
for increasing legilibilty at small
scale (1:250 000 )
Approaches
Segmentation U-Net (Ronneberger et al., 2015)
Generation Pix2Pix (Isola et al, 2017)
CycleGAN (Zhu et al. 2017)

16. Method : an exploration through three use
cases
Exploring the potential of deep learning for map generalization
16
Generalizing urban areas
in topographic maps at
medium scale (1:50 000)
Approaches
Generation Pix2Pix (Isola et al, 2017)
CycleGAN (Zhu et al. 2017)
FuseGAN
Mixed DeepMapScaler

17. Method : an exploration through three use
cases
Exploring the potential of deep learning for map generalization
17
Predicting necessary
information for map
generalization Approach
Node
classification
GCN (Kipf & Welling, 2016)

18. Outline
Exploring the potential of deep learning for map generalization
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A. Dataset
Quality
Representation
B. Evaluation
Why and how ?
Raster-based
C. Integration
Usage
Combination
Conclusion

19. Dataset
Exploring the potential of deep learning for map generalization
19

20. A. Dataset
Exploring the potential of deep learning for map generalization
20
1. Quality
CycleGAN prediction

21. A. Dataset
Exploring the potential of deep learning for map generalization
21
1. Quality
Input Prediction Target
CycleGAN prediction
1km

22. A. Dataset
Exploring the potential of deep learning for map generalization
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2. Representation
Layered representation
For each cartographic theme, shape and position of entities are represented using one mask. Masks are stacked
in an image with n (the number of themes) layers.
Symbolized
A cartographic symbol is applied to
each entities, the entities are
rasterized in a map looking image.

23. A. Dataset
23
2. Representation
Pix2Pix prediction

24. A. Dataset
24
2. Representation
Exploring the potential of deep learning for map generalization
Symbolized representation Layered representation

25. A. Dataset
Exploring the potential of deep learning for map generalization
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2. Representation

26. A. Dataset
Exploring the potential of deep learning for map generalization
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2. Representation
Input Target
Additionnal
information
Stack model
prediction
Pix2Pix prediction
Fusion model
prediction
FusePix prediction
Prediction
Pix2Pix prediction

27. A. Dataset
27
2. Representation
Stack model prediction Fusion model prediction
Target
Exploring the potential of deep learning for map generalization

28. Evaluation
Exploring the potential of deep learning for map generalization
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29. B. Evaluation
Exploring the potential of deep learning for map generalization
29
1. Why and how ?
Tuning
Training set
preparation
Setting
Weight
adjustment
Controlling
Training
Validate
Testing

30. B. Evaluation
Exploring the potential of deep learning for map generalization
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1. Why and how ?
Automatic Manual
Map generalization Constraint violation measure Expert evaluation
Deep learning Similarity estimation User preference test

31. B. Evaluation
Exploring the potential of deep learning for map generalization
31
2. Raster-based evaluation
Clutter Reduction
Smoothness
Coalescence Reduction
Legibility
Position Preservation
Road Connectivity
Preservation
Preservation
Color Realism
Noise Absence
Realism

32. B. Evaluation
Exploring the potential of deep learning for map generalization
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2. Raster-based evaluation
Dilation N
Erosion N+6
Dilation 6
Coalescence
measure

33. B. Evaluation
Exploring the potential of deep learning for map generalization
2. Raster-based evaluation
33

34. Integration
Exploring the potential of deep learning for map generalization
34

35. C. Integration
Exploring the potential of deep learning for map generalization
35
1. Usage
(Touya et al., 2018) (Yan et al., 2020)
• The model is trained to
predict information about
geographic entity.
• This information is used by
generalization operators.
Deep data
enrichment
(Courtial et al., 2021)

36. C. Integration
Exploring the potential of deep learning for map generalization
36
1. Usage
(Courtial et al., 2020)
(Feng et al., 2019) (Du et al., 2021)
• The model is trained to
predict the generalization
of an entity or a group of
entities.
• The prediction must be
integrated in a map.
Deep generalization
operator

37. C. Integration
Exploring the potential of deep learning for map generalization
37
1. Usage
Some examples
(Courtial et al., 2021)
(Kang et al., 2019) (Isola et al., 2017)
• The model is trained to
predict the generalized
map of an area.
Deep map generation

38. C. Integration
Exploring the potential of deep learning for map generalization
38
2. Combination
MAP
Map generation
B C
...
Deep generalization operator
A
...
Deep data enrichment
Vector
database
Keys
Training set
Deep learning model
Layered representation
A : of additionnal information
B: of main information
C: of generalized themes

39. C. Integration
Exploring the potential of deep learning for map generalization
2. Combination
39
GAN MAP
Map generation
Generalized
building
Generalized
water
Generalized
Road
GAN
GAN
Fuse
GAN
Deep generalization operator
Main
information
with a layered
representation
GCN
Road network
graph
Block graying
Deep data enrichment
Building and
road images
UNet
Road
importance
Water
Building
Road

40. C. Integration
Exploring the potential of deep learning for map generalization
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2. Combination
Expliquer plus
Add input
Input Workflow prediction Unique model prediction Target

41. C. Integration
Exploring the potential of deep learning for map generalization
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2. Combination
Input Workflow prediction Unique model prediction
Displacement
Both ?
Amalgamation

42. C. Integration
Exploring the potential of deep learning for map generalization
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2. Combination
• Independent representation
• Simpler evaluation and post
processing
• Independent training
• Allows to learn new
operation
• Requires more time and
storage capacity
• The propagation of errors is
more probable

43. Conclusion
Exploring the potential of deep learning for map generalization
43

44. Conclusion
Exploring the potential of deep learning for map generalization
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Can deep learning contribute to map generalization ?
Graph-based approach
Image-based approach
For data enrichment
Graph-based approach
Image-based approach
For operator learning
With a unique model
With a workflow
For generalized map generation

45. Exploring the potential of deep learning for map generalization
45
Conclusion
Perspectives

46. Conclusion
Exploring the potential of deep learning for map generalization
46
Perspectives
Graph based shape prediction Interpolation of spatial relation

47. Conclusion
Exploring the potential of deep learning for map generalization
47
Perspectives
(Liu
et
al.,
2021)
s

48. Thank you !