Raster datasets represent geographic features by dividing the world into discrete square or rectangular cells laid out in a grid. 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 are commonly used for representing and managing imagery, digital elevation models, and numerous other phenomena. Often, rasters are used as a way to represent point, line, and polygon features. In the example below, you can see how a series of polygons would be represented as a raster dataset.
Rasters can be used to represent all geographic information (features, images, and surfaces), and they have a rich set of analytic geoprocessing operators. 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 in the geodatabase
A raster is a set of cells arranged in rows and columns and is a commonly used dataset in GIS. Users typically employ many raster files, yet many users see an increasing need to manage raster data, along with their other geographic information, in a DBMS. The geodatabase provides a very effective means for raster data management in both file and enterprise geodatabases.
Raster management strategies
Two data management strategies for rasters are important:
- Raster provisioning—Getting raster datasets quickly into play in your GIS means you will most likely use them as is, typically as a series of raster files. This can be a series of independent files, or you can use a technology like the Image extension to ArcGIS for Server to manage and serve these existing datasets as a collection.
- Rasters in the geodatabase—This strategy is useful when you want to manage rasters, add behavior, and control the schema; want to manage a well-defined set of raster datasets as part of your DBMS; need to get high-performance without loss of content and information (no compression); and want one data architecture for managing all your content.
Geographic properties of raster data
Four geographic properties are typically recorded for all raster datasets. These become useful for georeferencing and help explain how raster data files are structured. This concept is important to understand: it helps explain how rasters are stored and managed in the geodatabase.
Raster datasets have a special way of defining geographic location. 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. This means that each raster dataset typically has a header record holding its geographic properties, and the body of the content is an ordered list of cell values.
Geographic properties for a raster typically include the following:
- Its coordinate system
- A reference coordinate or x,y location (typically the upper left or lower left corner of the raster)
- A cell size
- The count of rows and columns
This information can be used to find the location of any specific cell. 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.
The raster block table in the geodatabase
Raster data is typically much larger in size than features and requires a side table for storage. For example, a typical orthoimage can have as many as 6,700 rows by 7,600 columns (more than 50 million cell values).
To get high performance with these larger raster datasets, a geodatabase raster is cut into smaller tiles (referred to as blocks) with a typical size of around 128 rows by 128 columns or 256 by 256. These smaller blocks are then held in a side table for each raster. Each separate tile is held in a separate row in a block table as shown below.
This structure means that only the blocks for an extent need to be fetched when they are needed instead of the entire image. In addition, resampled blocks used to build raster pyramids can be stored and managed in the same block table as additional rows.
This enables rasters of enormous sizes to be managed in a DBMS; produce very high performance; and provide multiuser, secure access.
Rasters are heavily and increasingly used in GIS applications. The geodatabase can manage rasters for many purposes: as individual datasets and as logical collections of datasets called a mosaic.
|Methods of storage||Description|
A raster dataset is any valid raster format organized into one or more bands. Each band consists of an array of pixels (cells) and each pixel has a value. A raster dataset has at least one band. More than one raster dataset can be spatially appended (mosaicked) together into a larger, single, continuous raster dataset.
A mosaic dataset is a collection of raster datasets (images) stored as a catalog and viewed or accessed as a single mosaicked image or individual images (rasters). These collections can be extremely large, both in total file size and number of raster datasets. The raster data is added according to its raster type, which identifies metadata, such as georeferencing, acquisition date, and sensor type, along with a raster format. The raster datasets in a mosaic dataset can remain in their native format on disk or, if required, be loaded into the geodatabase. The metadata can be managed within the raster record as well as attributes in the attribute table. Storing metadata as attributes enables parameters such as sensor orientation data to be managed easily as well as enabling fast queries to enable selections.
Using mosaic in ArcGIS
There are several ways in which the term mosaic is used in ArcGIS: