RasterFrames® brings together Earth-observing (EO) data analysis, big data computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is Big Data in the truest sense, and its footprint is rapidly getting bigger.
RasterFrames provides a DataFrame-centric view over arbitrary EO data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is accessible to general analysts while handling the rapidly growing data footprint.
To learn more, please see the Getting Started section of this manual.
The source code can be found on GitHub at locationtech/rasterframes.
- Related Links
- Getting Started
- RasterFrames in Python
- Creating RasterFrames
- Spatial Queries
- Machine Learning
- Exporting RasterFrames
- Release Notes