v1.4.0

The main change is that now a demo is available, you can now execute the full workflow on your own machine with your own data.

v1.3.0

Merge pull request #7 from KhaosResearch/v1.3

V1.3

v1.2.0

Some important changes:

• Now seasons are not hardcoded, instead defined in a json file referenced in SEASONS_FILE environment variable.
• It is possible now to use any number of seasons or any date interval for using landcoverpy
• Added seasons example files
• Improved RAM usage when creating the tile dataframe, now pd.concat is not used.
• Beta windowing read-write in prediction is now available! Right now it has only been tested splitting the rasters in (5,5) slices, since there are problems with windowing different spatial resolution images, i.e. 20m 10m sentinel2-bands.
• An example for calling the predict workflow using windowing is workflow(execution_mode=ExecutionMode.LAND_COVER_PREDICTION, window_slices=(5,5)). This reduces maximum RAM usage from 64GB to 8GB.
• It is pending to improve the windowing allowing different window slices OR allow using use_block_windows.
• Workflow main script splitted in train predict to improve readibility

v1.1

Some important changes:

• Allowed data inputs are now CSV, GeoJSON and KMZ. Only data points are allowed, not polygons.
• Input data should be only one file now.
• Second level classification has been optimized and generalized instead of being fixed to forest classification

v1.0.0

Full Changelog: /~https://github.com/KhaosResearch/landcoverpy/commits/v1.0.0