HDF5 (Hierarchical Data Format 5) is a file format and data model designed to store and organize large amounts of numerical data efficiently. Due to its ability to handle complex, hierarchical datasets, it is widely used in scientific computing, engineering, and machine learning.
HDF5 data is organized in a tree structure:
- Groups are like folders and subfolders.
- Datasets are like files, which can contain any binary data, along with attributes to store the metadata for the groups or datasets.
HDF5 is scalable, portable, self-describing, compressible, and partial I/O for efficient reading of large datasets. It is used for scientific computing, machine learning, simulation models, geographic, and climate data.
The HDF5 Raster reader can read multidimensional arrays of numerical data and interpret them as raster bands. A single HDF5 file can contain several arrays of bands, known as sub datasets, that can have different sizes and dimensions.
HDF5 is a very flexible and extendable data format with no universal way to store georeference data. The HDF5 Raster format does support some product types for georeferencing:
- HDF5 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24km (Level-2 OMTO3)
- HDF-EOS5 grids
- HDF-EOS5 swaths
Reader Overview
FME considers a single HDF5 file to be a dataset. One raster feature is read for each subdataset in the dataset.
FME Raster Features
FME raster features represent raster data and use several concepts that are unlike those used in the handling of vector data.
For comprehensive information about how FME processes raster data, see Rasters.