PostGIS Raster : Extending PostgreSQL for The Support of ... - CoDE

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sdo_geor.importFrom(geor, ’blocksize=(256,256)’, ’TIFF’, ’file’, ’srtm_new.tif’); UPDATE spain_images SET image = geor WHERE image_id = 1; END; The same operation is repeated for every image file. If these images form a non-continuous coverage, two new tables are needed for each image file. Loading Using PostGIS Raster When loading raster data by using GDAL driver, the loader creates all that are necessary to store the raster data into a SQL file. It consists of creating the needed tables, the indexes (if it is specified) and inserting raster data into the tables. A single or a set of raster images can be loaded all only once. Example: python raster2pgsql.py -r path\*.tif -t srtm_tiled -s 4326 -k 50x50 -I -M -o srtm.sql Next step is to execute the srtm.sql file using psql: psql -d postgis_db -f srtm.sql -U user -W 3.2.6 Intersecting Raster and Vector Layers Intersecting in Oracle GeoRaster It is impossible to intersect directly raster and vector data in Oracle GeoRaster. Instead, what user can do is to select a sampling window to clip an area of the raster and get raster data over this area. The sampling windows must be rectangular or the round windows cannot be used (As described in SDO_GEOR.generateStatistics documentation 2 ). So the MBR is used as solution. In the following example, the sampling windows are the geometry objects stored in sampling_win table. The request will calculate the intersection between the sampling windows and the spatial extent of the GeoRaster objects [15]: DECLARE cellCoordinate mdsys.sdo_geometry; ret varchar2(256); gr sdo_georaster; BEGIN -- Intersect spatial extent of the rasters with the sampling windows (SELECT t.id, t.geom, r.image_id as rid FROM spain_images r, sampling_win t WHERE sdo_geom.sdo_intersection(r.image.spatialExtent, t.geom, 0.005) is not null) END; Intersecting in PostGIS Raster PostGIS Raster uses st_intersection() and st_intersects() functions, that operate directly on raster and vector data, to calculate the intersection. Before doing the intersection, the st_intersects() will check which portion of raster is intesected by vector. So the st_intersection() intersects vector with raster by vectorizing only the necessary part of the raster [8]: CREATE TABLE vector_srtm_inter AS SELECT id, (gv).geom AS the_geom, (gv).val FROM ( SELECT id, ST_Intersection(rast, the_geom) AS gv FROM srtm_tiled, vector_data WHERE ST_Intersects(rast, the_geom) ) inter; 2 http://docs.oracle.com/cd/B28359_01/appdev.111/b28398/geor_ref.htm#CHEDCICI. 25
In summary, the intersection is much slower than one in GeoRaster for two reasons: • To intersect vector and raster data, firstly raster has to be polygonized, then the result is the intersection between these polygons with vector data. In this process, polygonizing is a complex operation. • In Oracle, the raster data is sampled using rectangular windows. So the intersection is only performed between these MBRs with vector data while the vector data intersected by the raster data in PostGIS Raster are in some forms [15]. 26
Page 1 and 2: Faculté de Sciences Appliquées Se
Page 3 and 4: Abstract Nowadays, in many applicat
Page 5 and 6: 3.2.4 Overlapping Raster and Vector
Page 7 and 8: D.5 Geology, Geophysics and Geochem
Page 9 and 10: 4.9 Raster cell [16]. . . . . . . .
Page 11 and 12: Chapter 1 Introduction 1.1 Context
Page 13 and 14: Chapter 2 Geographic Information Sy
Page 15 and 16: 2.2.3 Imagery Figure 2.3: Polygon f
Page 17 and 18: Figure 2.8: Surface expressed by co
Page 19 and 20: 4-foot logo. The file size is the s
Page 21 and 22: 2.4.2 Data Theme Figure 2.14: Multi
Page 23 and 24: measures of latitude. Longitude mea
Page 25 and 26: 2.6 GIS Applications 2.6.1 Crime Ma
Page 27 and 28: 2.6.3 Traditional Knowledge GIS Tra
Page 29 and 30: These additional functionalities ma
Page 31 and 32: Figure 3.3: Physical storage of Geo
Page 33: DROP INDEX spain_images_idx; CREATE
Page 37 and 38: Attribute information Table 4.1: Po
Page 39 and 40: Figure 4.2: Information representat
Page 41 and 42: for discrete data while floating-po
Page 43 and 44: the matrix are parallel to the x-ax
Page 45 and 46: Lines Figure 4.15: Raster point rep
Page 47 and 48: Choosing an appropriate cell size i
Page 49 and 50: The multi-band conception is result
Page 51 and 52: "32BUI" = 32-bit unsigned integer "
Page 53 and 54: Figure 4.30: Pyramids at different
Page 55 and 56: ectangular because there might be s
Page 57 and 58: Figure 4.39: Vector to raster conve
Page 59 and 60: Figure 4.42: Web application with o
Page 61 and 62: In this example, the dummy_rast tab
Page 63 and 64: One approach to make this intersect
Page 65 and 66: 4.19 Intersection The fact that Pos
Page 67 and 68: Figure 4.51: Raster and raster inte
Page 69 and 70: ST_Present and ST_Passes predicates
Page 71 and 72: Chapter 5 User Guide Sometimes stor
Page 73 and 74: SELECT rast As origin, ST_Resample(
Page 75 and 76: • gvSIG plugin • MapServer thro
Page 77 and 78: SELECT ST_AsJPEG(rast) As rastjpg F
Page 79 and 80: FROM buff_temp GROUP BY id ORDER BY
Page 81 and 82: Chapter 6 Application Most of GIS a
Page 83 and 84: 6.2 Tools Figure 6.3: PostGIS conne
Page 85 and 86:
Figure 6.6: Band area. controlled b
Page 87 and 88:
Figure 6.10: The loading map parall
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Figure 6.14: Maps representing worl
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Figure 6.17: Maps with a color lege
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their levels of awareness of geogra
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[15] Jorge Arevalo. PostGIS Raster
Page 97 and 98:
Appendix A PostGIS Raster Utilisati
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eturn "Version 0.1" def qgisMinimum
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Appendix C Code Implementation C.1
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name="" # Setting table name and mo
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Figure D.1: Synthetic aperture rada
Page 107 and 108:
D.4 Digital Image Processing D.4.1
Page 109 and 110:
Figure D.4: Age of the sea floor. M
Page 111 and 112:
Appendix F Continuous Surfaces One
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PostGIS Raster : Extending PostgreSQL for The Support of ... - CoDE

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