RasterFrames™ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of GeoTrellis.
The source code can be found on GitHub at locationtech/rasterframes.
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 (UDT) 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.
To learn more, please see the Getting Started section of this manual.
RasterFrames™ is a new project under active development. Feedback and contributions are welcomed as we look to improve it. Please submit an issue if there’s a particular feature you think should be included.
- Related Links
- Getting Started
- RasterFrames in Python
- Creating RasterFrames
- Spatial Queries
- Machine Learning
- Exporting RasterFrames
- Release Notes