This enables rasters of enormous sizes to be managed in a DBMS produce very high performance and provide multiuser, secure access. In addition, resampled blocks used to build raster pyramids can be stored and managed in the same block table as additional rows. This simple structure means that only the blocks for an extent need to be fetched when they are needed instead of the entire image. Each separate tile is held in a separate row in a block table as shown below. These smaller blocks are then held in a side table for each raster. To get high performance with these larger raster datasets, a geodatabase raster is cut up into smaller tiles (referred to as blocks) with a typical size of around 128 rows by 128 columns or 256 x 256. For example, a typical orthoimage can have as many as 6,700 rows by 7,600 columns (more than 50 million cell values). Raster data is typically much larger in size than features and requires a side table for storage. The raster block table in the geodatabase By having this information available, the raster data structure lists all the cell values in order from the upper left cell along each row to the lower right cell, as illustrated below. This information can be used to find the location of any specific cell.
Once the cells or pixels can be accurately georeferenced, it's easy to have an ordered list of all the cell values in a raster. Raster datasets have a special way of defining geographic location. This concept is important to understand: it helps explain how rasters are stored and managed in the geodatabase.
These become useful for georeferencing and help explain how raster data files are structured.
This can be a series of independent files, or you can use a technology like ArcGIS Image Server to manage and serve these existing datasets as a collection.
In addition to being a universal data type for holding imagery in GIS, rasters are also heavily used to represent features, enabling all geographic objects to be used in raster-based modeling and analysis. Rasters can be used to represent all geographic information (features, images, and surfaces), and they have a rich set of analytic geoprocessing operators. In the example below, you can see how a series of polygons would be represented as a raster dataset. Often rasters are used as a way to represent point, line, and polygon features. Raster datasets are commonly used for representing and managing imagery, digital elevation models, and numerous other phenomena. Each cell has a value that is used to represent some characteristic of that location, such as temperature, elevation, or a spectral value. Raster datasets represent geographic features by dividing the world into discrete square or rectangular cells laid out in a grid.