Commit 168ac5cd authored by Leandro Parente's avatar Leandro Parente

pyeumap Workflow

parent 3b8c80db
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[![GitLab license](img/apache2.svg)](./LICENSE)
[![CRAN Status Badge](http://www.r-pkg.org/badges/version/eumap)](http://cran.r-project.org/web/packages/eumap)
[![PyPI version](https://badge.fury.io/py/eumap.svg)](https://pypi.python.org/pypi/eumap/)
[![Zenodo dataset](https://zenodo.org/badge/DOI/10.5281/zenodo.4058447.svg)](http://doi.org/10.5281/zenodo.4058447)
[Community](https://opendatascience.eu) |
[Documentation](https://eumap.readthedocs.org) |
......@@ -13,6 +14,21 @@ eumap is a library to enable easier access to EU environmental maps and function
It implements efficient spatial and spatiotemporal overlay, High Performance Computing, extends the Ensemble Machine Learning algorithms developed within the [mlr3](https://mlr3.mlr-org.com/) framework.
eumap builds upon various existing softare, especially on GDAL, R and Python packages for spatial analysis and Machine Learning libraries.
pyeumap Workflow
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![pyeumap Workflow](img/pyeumap_workflow.png)
The workflow implemented by pyeumap 1) fills all the gaps for diferent remote sensing time-series, 2) does the space time overlay of point samples on several raster layers according to the reference date, 3) trains and evaluate a machine learning model, and 4) does the space time predictions for a specific target variable. These processing steps are [demonstrate](demo/python) using a [benchmark dataset for land-cover classification](http://doi.org/10.5281/zenodo.4058447) in diferent areas of the EU
This 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.
![pyeumap Workflow](img/gapfill_example.png)
This 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.
![pyeumap Workflow](img/land_cover_predictions.gif)
License
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© Contributors, 2020. Licensed under an [Apache-2](https://gitlab.com/geoharmonizer_inea/eumap/blob/master/LICENSE) license.
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