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A Python library for n-dimensional Earth observation data processing

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This package contains a selection of tools to handle and analyze satellite data.

nd is making heavy use of the xarray library. dask is used for parallelization.

The GDAL library is only used as a compatibility layer in to enable reading supported file formats. Internally, all data is passed around as xarray Datasets and all provided functions expect this format as inputs. may be used to convert any gdal.Dataset object or GDAL-readable file into an xarray Dataset.


Several functions to read and write satellite data.

  • to/from NetCDF
  • read data from open GDAL datasets and any GDAL-readable file
  • deal with complex-valued data (not supported by NetCDF) by disassembling into two reals when writing to NetCDF, and vice versa when reading.


A module implementing change detection algorithms.

  • convert dual polarization data into the complex covariance matrix representation
  • OmnibusTest (change detection algorithm by Conradsen et al. (2015))


A collection of classification and clustering methods.

... work in progress ...


Implements several filters, currently:

  • kernel convolutions
  • non-local means


Several utility functions.

  • split/merge numpy arrays, xarray datasets, ...
  • parallelize operations acting on xarray datasets


Given a dataset with Ground Control Points (GCPs), usually in the form of a tie point grid, warp the dataset onto an equirectangular projection (WGS84), such that lat/lon directly correspond to the y and x coordinates, respectively.

This makes concatenating datasets easier and reduces storage size, because lat/lon coordinates do not need to be stored for each pixel.


Several functions to quickly visualize data.

  • create RGB images from data
  • create video from a spatiotemporal dataset


  • Split a dataset into tiles.
  • Read a tiled dataset.
  • Map a function across a tiled dataset.
  • Create and merge tiles with buffer to avoid edge affects.

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