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Python library for standardising satellite imagery into an Analysis Ready Data (ARD) form
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# wagl ------ wagl is a Python package for producing standarised imagery in the form of: * Nadir Bi-directional Reflectance Distribution Function Adjusted Reflectance (NBAR) * NBART; NBAR with Terrain Illumination correction * Surface Brightness Temperature * Pixel Quality (per pixel metadata) The luigi task workflow for producing NBAR for a Landsat 5TM scene is given below. ![](docs/source/diagrams/luigi-task-visualiser-reduced.png) ## Supported Satellites and Sensors ----------------------------------- * Landsat 5 TM * Landsat 7 ETM * Landsat 8 OLI * Landsat 8 TIRS * Sentinel-2a ## Requirements --------------- * [luigi](https://github.com/spotify/luigi) * [numpy](https://github.com/numpy/numpy) * [scipy](https://github.com/scipy/scipy) * [numexpr](https://github.com/pydata/numexpr) * [pyephem](http://rhodesmill.org/pyephem/) * [proj](https://github.com/OSGeo/proj.4) * [h5py](https://github.com/h5py/h5py) * [tables](https://github.com/PyTables/PyTables) * [pandas](https://github.com/pandas-dev/pandas) * [scikit-image](https://github.com/scikit-image/scikit-image) * [GDAL](https://github.com/OSGeo/gdal) * [rasterio](https://github.com/mapbox/rasterio) * [fiona](https://github.com/Toblerity/Fiona) * [shapely](https://github.com/Toblerity/Shapely) * [geopandas](https://github.com/geopandas/geopandas) * [pyyaml](https://github.com/yaml/pyyaml) * [attrs](https://github.com/python-attrs/attrs) ## Installation --------------- ### wagl Package The wagl pacakage can be installed via: `$ python setup.py install --prefix=<prefix>` ### Additional HDF5 compression filters (optional) Additional compression filters can be used via HDF5's [dynamically loaded filters](https://support.hdfgroup.org/HDF5/doc/Advanced/DynamicallyLoadedFilters/HDF5DynamicallyLoadedFilters.pdf). Essentially the filter needs to be compiled against the HDF5 library, and installed into HDF5's plugin path, or a path of your choosing, and set the HDF5_PLUGIN_PATH environment variable. The filters are then automatically accessible by HDF5 via the [integer code](https://support.hdfgroup.org/services/contributions.html) assigned to the filter. #### Mafisc compression filter Mafisc combines both a bitshuffling filter and lzma compression filter in order to get the best compression possible at the cost of lower compression speeds. To install the `mafisc` compression filter, follow these [instructions](https://wr.informatik.uni-hamburg.de/research/projects/icomex/mafisc). #### Bitshuffle The [bitshuffle filter](https://github.com/kiyo-masui/bitshuffle) can be installed from source, or conda via the supplied [conda recipe](https://github.com/kiyo-masui/bitshuffle/tree/master/conda-recipe). It utilises a bitshuffling filter on top of either a lz4 or lzf compression filter. ## Basic command line useage -------------------------- Using the [local scheduler](http://luigi.readthedocs.io/en/stable/command_line.html): $ luigi --module wagl.multifile_workflow ARD --workflow NBAR --level1-list scenes.txt --outdir /some/path --local-scheduler --workers 4 Using the [central scheduler](http://luigi.readthedocs.io/en/stable/central_scheduler.html): $ luigid --background --pidfile <PATH_TO_PIDFILE> --logdir <PATH_TO_LOGDIR> --state-path <PATH_TO_STATEFILE> $ luigi --module wagl.multifile_workflow ARD --level1-list scenes.txt --workflow STANDARD --outdir /some/path --workers 4 $ luigi --module wagl.multifile_workflow ARD --level1-list scenes.txt --workflow NBAR --outdir /some/path --workers 4 $ luigi --module wagl.multifile_workflow ARD --level1-list scenes.txt --workflow SBT --outdir /some/path --workers 4
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Python library for standardising satellite imagery into an Analysis Ready Data (ARD) form
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