Overview research line 2

Transcript

1. 3D4EM Storage, maintenance and dissemination Hugo Ledoux TU Delft 3D4EM kickoff meeting Rotterdam, 2015-01-14
7. Storage
8. What 3D objects do we have? 8 • 3D buildings in LOD0/1/2 → PostGIS • vegetations (in diff LODs?) → PostGIS • terrain • 3D roads • canals/water → 15M Top10NL objects → 1B+ BGT objects → 640B AHN2 elevation points TIN in a DBMS = complex } }“easy”
9. Massive TINs in a DBMS 9
15. 15 p a b c d e f
16. 16 p a b c d e f
17. 17 p a b c d e f star(p) = abcdef
18. and we store the star for every vertex 18
19. and we store the star for every vertex 19
20. and we store the star for every vertex 20
21. and we store the star for every vertex 21
22. Stars in a DBMS ID x y z star 1 3.21 5.23 2.11 2–44–55–61–23 2 5.19 29.01 4.55 7–98–111–233–222 3 22.43 15.99 8.19 99–101–73–23 ... ... ... ... ... 5674 221.19 15.23 37.81 309–802–793–1111 Advantages: 1 Only one table with id x y z star 2 No spatial index needed: fetching of triangles based on “walking” 3 Star column need not be filled (⇠ Simple Features) 4 Local updates are possible (insertion and removals) 5 Ideas are readily extensible to 3D for storing manipulating tetrahedra
23. Research objectives 23 1. massive TINs (1% of 640B = 6.4B points) 2. constrained TINs 3. TINs with vertical faces and ‘overfolds’, ie having a full “3D TIN”, not only a 2.5D TIN
24. some preliminary results 24 Storing and analysing massive TINs in a DBMS with a star-based data structure 17 degree points duplicates triangles convexhull avg max AHN2 281 884 687 214 050 563 768 199 1 173 6.00 63 serpent 3 265 110 17 584 6 494 998 52 6.00 39 msh 283 213 392 0 566 426 669 113 6.00 141 Table 2 Details concerning the datasets used for the experiments; convexhull is the number of points that are on the boundary of the convex hull of the dataset. Storing and analysing massive TINs in a DBMS with a star-based data structure 19 star structure triangles SF table index total table index total AHN2 28 GB 4.8 GB 32.8 GB 64 GB 29 GB 93 GB serpent 325 MB 56 MB 381 MB 746 MB 329 MB 1075 MB msh 28 GB 4.8 GB 32.8 GB 64 GB 29 GB 93 GB Table 4 Size of the tables and the indexes in PostgreSQL for the datasets. of triangles is around twice as large as that of points), the main cause is the building of the GiST spatial index of PostGIS, which took 20 times more time.
25. some preliminary results 25 Storing and analysing massive TINs in a DBMS with a star-based data structure 17 degree points duplicates triangles convexhull avg max AHN2 281 884 687 214 050 563 768 199 1 173 6.00 63 serpent 3 265 110 17 584 6 494 998 52 6.00 39 msh 283 213 392 0 566 426 669 113 6.00 141 Table 2 Details concerning the datasets used for the experiments; convexhull is the number of points that are on the boundary of the convex hull of the dataset. Storing and analysing massive TINs in a DBMS with a star-based data structure 19 star structure triangles SF table index total table index total AHN2 28 GB 4.8 GB 32.8 GB 64 GB 29 GB 93 GB serpent 325 MB 56 MB 381 MB 746 MB 329 MB 1075 MB msh 28 GB 4.8 GB 32.8 GB 64 GB 29 GB 93 GB Table 4 Size of the tables and the indexes in PostgreSQL for the datasets. of triangles is around twice as large as that of points), the main cause is the building of the GiST spatial index of PostGIS, which took 20 times more time.
26. Maintenance
28. visible errors
29. Dissemination platform
30. www.3d4em.nl Select area: Choose format: Choose format: Download 3D TOPNL CityGML 2.0 VRML ArcGIS Applications: Noise modelling Wind modelling Flooding CityGML 3.0 size: voxel (netCDF) 5m x 5m x 5m Specifications: 2.5 TIN trees buildings LOD1 buildings LOD2 Save
31. A typical Dutch landscape 31 hill tree house sidewalk
32. 2.5D terrain + separate 3D objects 32
33. 2.5D terrain + separate 3D objects (LOD1) 33
34. 3D terrain + separate 3D objects 34
35. 3D DSM (digital surface model) 35
36. 2.5D DSM 36
37. 3D grid (voxel) 37
38. thank you. h.ledoux@tudelft.nl tudelft.nl/hledoux Hugo Ledoux