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Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation
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
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