Slides at ICC conferences, decembre 2021.

1. Can Graph
ConvolutionNetworks
LearnSpatial Relations ?
Azelle Courtial, Guillaume Touya , Xiang Zhang
LASTIG, Univ Gustave Eiffel, ENSG, IGN, France
1

2. Plan
• Introduction
• Motivation : Graph modelisation of geographic information.
• Definitions : Graph and deep learning principle.
• Methods : Graph convolution network.
• Alignement detection
• Road selection
• Conclusion
2

3. Graph modelisation of geographic
information
3
(Mackaness and
Beard, 1993)
(Zhang et al., 2013)
(Bader et al., 2013)

4. Graph modelisation of geographic
information
Some geographic objects have a direct
representationas a graph:
transportationand hydrographic
networks.
Spatialrelationbetween
geographic object can be
model in a graph.​
The vector shape of
object can be encoding as
edge and node.
(Yan et al., 2020)
4

5. Attributes matrix
5

6. Adjacency matrix
6

7. Learning about graph
Node attribute prediction
Graph classification
Link prediction
7

8. Deep learning principles
Input
examples Graph convolution
Target
examples
Prediction
Comparison
8
Weight adjustement

9. Deep learning principles
Input
examples Graph convolution
Target
examples
Prediction
Comparison
9
Weight adjustement

10. Graph convolution
10
Deep series of informationencoding and decoding layer.
Attribut
matrix
× Adjacency matrix
× Weight

11. Plan
• Introduction
• Motivation : Graph modelisation of geographic information.
• Definitions : Graph and deep learning principle.
• Methods : Graph convolution network.
• Alignement detection
• Road selection
• Conclusion
12

12. Alignement detection
Experiment
1 2
3 4
13

13. Alignement detection
Proximity structures
Proximity criteria
Remove edge with a distanceabove
a threshold.
Add edge with a low weight
comparing to the weight of
the shortest path between its nodes.
Criteria from (Bader et al., 2005)
14

14. 15
Alignement detection

15. Alignement detection
Attributes
16

16. Alignement detection
Attributes
17
Elongation difference Area difference
Orientationdifference
Facing ratio
(Wang et al., 2019)

17. Alignement detection
Results
18
Annotation /
Prediction
Curvilinear
alignements
Straight
alignements
No
alignements
Total
Curvilinear
alignements
1125 34 1412 2571
Straight
alignements
66 1196 2662 3924
No
alignement
819 1746 9919 12484
Total 2010 2976 13993 18979

18. Alignement detection
Results
19

19. Plan
• Introduction
• Motivation : Graph modelisation of geographic information.
• Definitions : Graph and deep learning principle.
• Methods : Graph convolution network.
• Alignement detection
• Road selection
• Conclusion
20

20. Road selection
21
Generalised road network (for 1:50k)
Detailedroad network (for 1:25k)

21. Road selection
Structure change cases
22
Erased at target scale
Kept at target scale
Annotation

22. Road selection
Attributes
23
Edge length Importance Sinuosity
Betweness centrality Face size Stroke length

23. Road selection
Results
24

24. Road selection
Results
25
Annotation
/ Prediction
Deleted Kept Total
Deleted 3098 1280 4378
Kept 2313 6246 8559
Total 5411 7526 12937
72% of section are well classified
Precision: 73%
Recall : 83%

25. Conclusion and perspective
26

26. Thank you for your attention
27