RasterFrames® enables analysts, data scientists and EO specialists to easily work with EO data in the familiar DataFrames data structure. Furthermore, it enables solutions to scale from interactive exploration of imagery to processing arbitrarily large data sets, horizontally scaling compute from the laptop to the supercomputer. It is available under the Apache 2.0 license.

locationtech project


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RasterFrames brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. It provides a DataFrame-centric view over arbitrary raster data in a horizontally scalable compute environment, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using the DataFrame as a unified cognitive and compute model, RasterFrames makes the rapidly growing EO data footprint accessible to general analysts and EO specialists in a form that scales from the laptop to the supercomputer.


Core features

  • Language Support

    • Python
    • Scala
    • SQL
  • Readers

    • Catalog-based Spark DataSource for heterogeneous multi-band raster data sets
    • GeoTIFF format via GeoTrellis JVM reader
    • GDAL-supported formats via GeoTrellis GDAL bindings
    • GeoTrellis layer reader
    • GeoJSON format via JTS parser
    • Landsat 8 and MODIS NBAR on AWS PDS catalog readers
  • Writers

    • GeoTIFF
    • GeoTrellis layers
    • Parquet compatible
  • Spatial Relations

    • Spatial relation query & filtering support via GeoMesa
    • Standard DE-9IM topological relations: Intersects, Contains, Within, Covers, etc.
    • Raster join between DataFrames of arbitrary raster data
    • Spatial join between raster and vector DataFrames
  • Operations

    • "Map Algebra"
    • Reprojection
    • Masking
    • Rasterization
    • NoData and cell-type handling
    • Spatio-temporal and metadata filtering
    • Local, zonal, and aggregate statistics
  • Interoperability

    • Spark Ecosystem, including Spark ML
    • Numpy tile encoding
    • Pandas conversions
    • GeoTrellis

Implemented Standards

  • Geographic JSON (GeoJSON)
  • Georeferenced Tagged Image File Format (GeoTIFF)
  • Well-Known Binary (WKB)
  • Well-Known Text (WKT)

Service Providers

Core contributors

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