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Martijn Meijers STW User Committee meeting , Utrecht, 19 September, 2012

Vario-scale (and nD extension): results and work in progress. Martijn Meijers STW User Committee meeting , Utrecht, 19 September, 2012. Contents. Creating smooth SSC Using SSC 3D vario-scale Conclusion tGAP = topological Generalized Area Partitioning (2D) SSC = Space Scale Cube.

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Martijn Meijers STW User Committee meeting , Utrecht, 19 September, 2012

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  1. Vario-scale (and nD extension): results and work in progress Martijn Meijers STW User Committee meeting, Utrecht, 19 September, 2012

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

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

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

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

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

  7. Implementation: simplification

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

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

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

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

  12. (n+1)D overlap selection for panning Normal panning Progressive panning (normal sorting order)

  13. 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;

  14. 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): GetCapabilities should indicate if server supports progressive refinement Reporting of the min and max imp of a theme New request type GetFeatureByImportance

  15. Example WFS-R request <wfs:GetFeatureByImportanceservice="WFS" version="1.0.0" outputFormat="GML2" ...> <wfs:Query typeName='tgap_face' minImp='5' maxImp=‘8'> <ogc:Filter> <ogc:BBOX> <ogc:PropertyName>geom</ogc:PropertyName> <gml:Box srsName=“…epsg.xml#28992"> <gml:coordinates> 136931,416574 139382,418904 </gml:coordinates> </gml:Box> </ogc:BBOX> </ogc:Filter> <ogc:SortBy>gdmc:imp_high D</ogc:SortBy> </wfs:Query> </wfs:GetFeatureByImportance>

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

  17. zii’’ zii’ zii ZII zi’’ zi’ zi 3D smooth merge No ZI

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

  19. ZII 3D Smooth simplify • Simplify boundary of merged object, two options: • 1. Keep block shape • 2. Tilted roof shape ZII’ ZII’’

  20. z y x s Pseudo 4D-view s1 s2 Zone zi shown for reference purpose ZII’’ zii zi

  21. Step-wise approach

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

  23. 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

  24. 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

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