This document discusses representing vector geographic data as tensors for use in deep learning-based map generalization. It presents problems with traditional vector representations, such as a lack of spatial context and overlapping geometries. The authors propose a layered tensor representation of the data that embeds additional contextual information. They conduct experiments on road network selection and alignment using their tensor-based approach and find it performs better than methods without the contextual information.

1. Representing Vector Geographic
Information As a Tensor for
Deep Learning-BasedMap
Generalization.
Azelle Courtial, Guillaume Touya​, Xiang Zhang
LASTIG, Univ. Gustav Eiffel, ENSG, IGN, Champs-sur-Marne, France
School of Geospatial Engineering and Sciences, Sun Yat-Sen University, Guangzhou, China

2. Content
• Context
• Problems: An in-adapted representationof geographic information
• From vector geographic database to Tensor-set: A layered and
contextual representation.
• Experiments
• Conclusion
2

3. 3
Map generalisation
Context

4. Context
4
Deep learning

5. 5
Context

6. 6
(Kang et al., 2019)
(Feng et al., 2019)
Relatedworks
Context
(Isolaet al., 2017)

7. 7
Deep learning for generalized map generation
Context
(Courtial et al., 2021)

8. 8
Spatial context.
Problems

9. 9
Attribute information
Problems

10. 1
0
Overlapping
Problems

11. 11
Tiling
From Vector Geographic Database to Tensor-set.

12. 12
Representationof raw information
From Vector Geographic Database to Tensor-set.

13. 13
Representationof additional information
Context Attribut
Context
and
Attribut
From Vector Geographic Database to Tensor-set.

14. 14
Representationof additional information
Ordered categories
Un-ordered categories
Quantity
From Vector Geographic Database to Tensor-set.

15. Architecture for tensor fusion
15
From Vector Geographic Database to Tensor-set.

16. 16
Prediction with layeredand symbolised representation
Experiments

17. 17
Calculateroads selection indicator.
Graph based learning
Experiments
(Courtial et al., 2022)

18. 18
Prediction with and without selection indicator
Experiment

19. Prediction with and without hint on alignement position
Experiment
19

20. Prediction with hint on alignementposition
Experiment
20
Errors in the white areas are doubled

21. Conclusion
1. Layered representation: The simplest is the best.
2. Additional information: ensuring necessary information is implicit in the data.
21
Future work
1. Layered architectures 2. Other additional information

22. Thank you !
22
Contact : azelle.courtial@ign.fr