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Spatiotemporal machine learning in Python (Part 1)

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Spatiotemporal machine learning in Python (Part 1)
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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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Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn) This tutorial covers the theoretical background for machine learning and python implementations, as well as integrating raster data with scikit-learn models. Why use pyeumap.LandMapper? The tutorial shows how to prepare the training samples with spatial overlay, how to evaluate the ML model performance with spatial cross-validation, how to tune the ML model with hyperparameter optimization, how to get the final ML model ,and finally how to generate spatial predictions using the fitted model.
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