Spatiotemporal raster
A raster is a grid — a matrix of cells (also called pixels) arranged in rows and columns. Each cell stores attribute values representing the information about the area it covers. These values are collectively called raster data.
Because raster data carries both spatial and temporal attributes, rasters are also called spatio-temporal rasters. The temporal dimension lets you use spatio-temporal raster data to manage time series.
Raster data types
Raster data falls into two categories:
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Thematic data: Each cell value represents a measurement or a classification. Examples include pollutant concentration, rainfall, land ownership types, and vegetation types.
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Image data: Also called remote sensing image, image data consists of films or images captured using ground remote sensing, aerial remote sensing, or aerospace remote sensing technologies. These images record the electromagnetic wave size of ground objects. Image data includes aerial images and remote sensing satellite images.
Lindorm and DLA GanosBase
Lindorm handles the storage and retrieval of spatio-temporal raster data. Combined with Data Lake Analytics (DLA) GanosBase, it provides a complete solution for processing spatio-temporal raster data across complex scenarios such as spatio-temporal raster computing.
For terminology used in this solution, see Terms.