Exploring the potential of deep learning for map generalization
- 1. Exploring the potentialof deep learning for map generalization Azelle Courtial Supervised by Guillaume Touya and Xiang Zhang
- 2. Context : mapgeneralization Exploring the potential of deep learning for map generalization 2
- 3. Context : mapgeneralization Exploring the potential of deep learning for map generalization 3 Operators Selection Enlargement Simplification Typification Amalgamation
- 4. Context : mapgeneralization Exploring the potential of deep learning for map generalization 4 Geometry, Attribute, Context, Etc. Multi criteria decision
- 5. Context : mapgeneralization Exploring the potential of deep learning for map generalization 5
- 6. Context : baseidea Exploring the potential of deep learning for map generalization 6 (Simo-serra et al. 2017)
- 7. Context : deeplearning principle Exploring the potential of deep learning for map generalization 7 NOT A MAP A MAP A MAP Input Prediction Target Model
- 8. Context : deeplearning principle Exploring the potential of deep learning for map generalization 8 1. Model 2. Training 3. Dataset 4. Loss function Issues
- 9. Issue 1. Designingan adapted deep neural network Exploring the potential of deep learning for map generalization 9
- 10. Issue 2. Trainingefficiently the model Exploring the potential of deep learning for map generalization 10
- 11. Issue 3. Designingan adapted training set Exploring the potential of deep learning for map generalization 11 Input Target
- 12. Issue 4. Measuringthe 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 potentialof deep learning for map generalization 13 Explore the potential of deep learning to contribute to map generalization research.
- 14. Method : anexploration through three use cases Exploring the potential of deep learning for map generalization 14
- 15. Method : anexploration 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 : anexploration 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 : anexploration 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 potentialof deep learning for map generalization 18 A. Dataset Quality Representation B. Evaluation Why and how ? Raster-based C. Integration Usage Combination Conclusion
- 19. Dataset Exploring the potentialof deep learning for map generalization 19
- 20. A. Dataset Exploring thepotential of deep learning for map generalization 20 1. Quality CycleGAN prediction
- 21. A. Dataset Exploring thepotential of deep learning for map generalization 21 1. Quality Input Prediction Target CycleGAN prediction 1km
- 22. A. Dataset Exploring thepotential of deep learning for map generalization 22 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 Pix2Pixprediction
- 24. A. Dataset 24 2. Representation Exploringthe potential of deep learning for map generalization Symbolized representation Layered representation
- 25. A. Dataset Exploring thepotential of deep learning for map generalization 25 2. Representation
- 26. A. Dataset Exploring thepotential of deep learning for map generalization 26 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 Stackmodel prediction Fusion model prediction Target Exploring the potential of deep learning for map generalization
- 28. Evaluation Exploring the potentialof deep learning for map generalization 28
- 29. B. Evaluation Exploring thepotential 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 thepotential of deep learning for map generalization 30 1. Why and how ? Automatic Manual Map generalization Constraint violation measure Expert evaluation Deep learning Similarity estimation User preference test
- 31. B. Evaluation Exploring thepotential 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 thepotential of deep learning for map generalization 32 2. Raster-based evaluation Dilation N Erosion N+6 Dilation 6 Coalescence measure
- 33. B. Evaluation Exploring thepotential of deep learning for map generalization 2. Raster-based evaluation 33
- 34. Integration Exploring the potentialof deep learning for map generalization 34
- 35. C. Integration Exploring thepotential 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 thepotential 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 thepotential 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 thepotential 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 thepotential 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 thepotential of deep learning for map generalization 40 2. Combination Expliquer plus Add input Input Workflow prediction Unique model prediction Target
- 41. C. Integration Exploring thepotential of deep learning for map generalization 41 2. Combination Input Workflow prediction Unique model prediction Displacement Both ? Amalgamation
- 42. C. Integration Exploring thepotential of deep learning for map generalization 42 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 potentialof deep learning for map generalization 43
- 44. Conclusion Exploring the potentialof deep learning for map generalization 44 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 potentialof deep learning for map generalization 45 Conclusion Perspectives
- 46. Conclusion Exploring the potentialof deep learning for map generalization 46 Perspectives Graph based shape prediction Interpolation of spatial relation
- 47. Conclusion Exploring the potentialof deep learning for map generalization 47 Perspectives (Liu et al., 2021) s
- 48.