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These processing steps are [demonstrated ](demo/python) using a [benchmark dataset for land-cover classification](http://doi.org/10.5281/zenodo.4058447) in different areas of the EU\n\nThis image presents the output of the gap filing approach for an area located in Croatia (tile 9529). This image refers to a Landsat temporal composites for the 2010 fall season, however all the 4 seasons since 2000 were analysed to fill the gaps.\n\n![pyeumap Workflow](img/gapfill_example.png)\n\nThis animation shows the land-cover classes for an area located in Sweden (tile 22497) according to the space time predictions. This example is a small use case that used 680 point samples, obtained in different years, to train a single model and to predict the land-cover in the region over the time.\n\n![pyeumap Workflow](img/land_cover_predictions.gif)\n\nSpatiotemporal Machine-Learning\n-------------------------------\nIn the Geo-harmonizer project, we prepare Analysis-Ready Earth Observation images from Landsat and \nSentinel missions, then use ground observations from the European Commission projects such as [LUCAS surveys](https://land.copernicus.eu/imagery-in-situ/lucas) and CORINE and similar to \noverlay the ground observations in the spacetime cubes. From this data we create spatiotemporal regression and classification matrices (see: [sample data set](https://doi.org/10.5281/zenodo.4058447)). 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