1. 28-10-2015 Challenge the future Delft University of Technology Martijn Meijers STW User Committee meeting, Utrecht, 19 September, 2012 Vario-scale (and nD extension): results and work in progress

2. 2 Vario-scale Contents Creating smooth SSC Using SSC 3D vario-scale Conclusion tGAP= topological Generalized Area Partitioning (2D) SSC= Space Scale Cube

3. 3 Vario-scale Smooth simplify Shock change: 2 rectangles 1 triangle Smooth change: 3 triangles Original line  Simplified  Transition 

4. 4 Vario-scale Smooth merge for convex neighbour 1.make #nodes shared top and target bnd equal (n) view 2.connect node pairs 3.2 triangles + n-3 quadrangles 4.if non-flat  space- split quadrangle scale into 2 triangle view 5.Merge planar neighbours

5. 5 Vario-scale Alternative (without adding nodes) LS_s = 7 LS_o = 1+3+1 = 5 b_s (shared boundary) b_o (opposite boundary) at scale s2, from s1 to s2, at scale s1 Create triangle base at one side and top at other side

6. 6 Vario-scale Non-convex neighbour  subdivide in convex parts m-shaped neighbourneighbour with hole (note: smooth collapse/split similar to smooth merge)

7. 7 Vario-scale Implementation: simplification

8. 8 Vario-scale Contents Creating smooth SSC Using SSC 3D vario-scale Conclusion tGAP= topological Generalized Area Partitioning (2D) SSC= Space Scale Cube

9. 9 Vario-scale Selection based on (n+1)D overlap from space-scale cube Simple initial mapProgressive initial map (sorting lower  higher detail) x y s High LoD  Low LoD 

10. 10 Vario-scale Selection based on (n+1)D overlap from space-scale cube Simple initial mapProgressive initial map (sorting lower  higher detail) x y s

11. 11 Vario-scale (n+1)D overlap selection for zooming Progressive zoom-inProgressive zoom-out (normal sorting order)(reverse sorting order)

12. 12 Vario-scale (n+1)D overlap selection for panning Normal panningProgressive panning (normal sorting order)

13. 13 Vario-scale

14. 14 Vario-scale Streaming of importance range (and first compared with a cut) A cut (or slice) of single importance A ordered range of importance values select face_id as id, '101' as impLevel, RETURN_POLYGON(face_id, 101) as geom from tgap_face where imp_low <= 101 and 101 < imp_high; select face_id as id, imp_high as impLevel, imp_low, imp_high, RETURN_POLYGON(face_id,imp_high) as geom from tgap_face where imp_high >= imp_high order by imp_high desc;

15. 15 Vario-scale Extension to OGC/ISO WFS It is possible to specify imp range in Filter part of GetFeature request and using ogc:SortBy Not ideal because it is not clear that this is about scale, streaming, progressive transfer/refinement Deeper integration in WFS (called WFS-Refinement): 1.GetCapabilities should indicate if server supports progressive refinement 2.Reporting of the min and max imp of a theme 3.New request type GetFeatureByImportance

16. 16 Vario-scale Example WFS-R request <wfs:GetFeatureByImportance service="WFS" version="1.0.0" outputFormat="GML2"...> geom 136931,416574 139382,418904 gdmc:imp_high D

17. 17 Vario-scale Contents Creating smooth SSC Using SSC 3D vario-scale Conclusion tGAP= topological Generalized Area Partitioning (2D) SSC= Space Scale Cube

18. 18 Vario-scale 3D smooth merge zi zii zi’’ zii’’ zi’ zii’ No ZI ZII

19. 19 Vario-scale Generic 3D smooth merge High detail zi’ zii’ 1 (bottom) 3 (top) 4 (back) 1 2 (front) 2 4 5 3 5 (left) c b d a C B A D e f 5 (left) zi zii 1 (bottom) 3 (top) 4 (back) 2 (front) Opposite boundary (5 faces) Equator (ring) zi’’ zii’’ Low detail

20. 20 Vario-scale 3D Smooth simplify ZII’’ ZII’ ZII Simplify boundary of merged object, two options: 1. Keep block shape 2. Tilted roof shape

21. 21 Vario-scale Pseudo 4D-view Zone zi shown for reference purpose x y z s zi zii ZII’’ s1s2

22. 22 Vario-scale Step-wise approach

23. 23 Vario-scale Contents Creating SSC Using SSC 3D vario-scale Conclusion tGAP= topological Generalized Area Partitioning (2D) SSC= Space Scale Cube

24. 24 Vario-scale Conclusion, advantages vario-scale maps Map producers: more integrity, less manpower needed for the production of maps (=less cost) Map providers: more efficiency, less data = less storage capacity, less energy, high transmission speed Map users: more functionality at less cost and higher speed Patent pending nr. OCNL 2006630

25. 25 Vario-scale Conclusions, true vario-scale True vario-scale nD maps based on (n+1)D representations and slicing (selecting) with hyperplanes: tGAP structure translates 2D space and 1D scale in an integrated 3D topological representation: no overlaps and no gaps (in space and scale) Starting with 3D space and adding scale results in 4D Starting with 3D space and time (history) and adding scale results in 5D topological structure (again no gaps/overlaps in space, time or scale), well defined neighbors in space, time and scale directions