RasterFrames brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis.
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The underlying purpose of RasterFrames is to allow data scientists and software developers to process and analyze geospatial-temporal raster data with the same flexibility and ease as any other Spark Catalyst data type. At its core is a user-defined type (UDF) called TileUDT, which encodes a GeoTrellis Tile in a form the Spark Catalyst engine can process. Furthermore, we extend the definition of a DataFrame to encompass some additional invariants, allowing for geospatial operations within and between RasterFrames to occur, while still maintaining necessary geo-referencing constructs.
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