We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Spatiotemporal machine learning in Python (Part 2)

228
 Cite
 Share
 Related Material
 Download
 Purchase
Logo of OpenGeoHub FoundationOpenGeoHub Foundation
 Parente, Leandro Diemen, Chris van

Formal Metadata

Title
Spatiotemporal machine learning in Python (Part 2)
Title of Series
Number of Parts
57
Author
License
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.
Identifiers
Publisher
Release Date
Language
Producer
Production PlaceWageningen

Content Metadata

Subject Area
Genre
Abstract
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, eumap, scikit-learn) This tutorial covers the theoretical background for Ensemble ML and python implementations, exploring the general concepts and main advantages of spatiotemporal machine learning. Why use LandMapper? The tutorial also shows how to prepare the training sample via spacetime overlay, how to evaluate the EML model performance via spacetime cross-validation, how to tune the EML model via hyperparameter optimization, to finally fit the final EML model.
Keywords