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Introduction to ODSE datasets in Python

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Logo of OpenGeoHub FoundationOpenGeoHub Foundation
 Parente, Leandro

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Introduction to ODSE datasets in Python
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57
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production PlaceWageningen

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Abstract
The first 30 minutes of this tutorial are dedicated to Software/libraries preparations and user support Software requirements: Python, Jupyter, QGIS, GRASS GIS, R. Following, the tutorial moves on to cover the introduction to spatial and spatiotemporal data in Python. Software requirements: opengeohub/py-geo docker image (gdal, rasterio, geopandas, eumap). The tutorial shows an introduction to JupytetLab and python libraries, introduction to spatiotemporal datasets, and explains the theoretical background for spatial and spatiotemporal machine learning. Then, the Eumap library (gapfilling and mapper modules), and some Eumap spatiotemporal datasets examples (land cover 2000-2020 training dataset by Witjes et al, 2021) are covered.
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