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Training & Hub
Resources
Hub & Training Materials
eo2cube Hub
Access our JupyterHub environment to run pre-configured notebooks directly in your browser — no local installation required. The eo2cube Hub provides a ready-to-use analysis environment with direct access to Earth Observation data cubes and all necessary libraries pre-installed.
Launch Hub
Training Notebooks
All training notebooks are openly available on the eo2cube GitHub organisation. Browse, fork, and run hands-on exercises covering data cube workflows, satellite time series analysis, and land cover classification — all built around real Earth Observation datasets.
View on GitHubCurriculum
What You Will Learn
Data Cube Basics
Understand the structure and principles behind Earth Observation data cubes and how they simplify large-scale satellite data access.
Sentinel Analysis
Work with Sentinel-1 radar and Sentinel-2 optical data to extract meaningful information about land cover and surface conditions.
Time Series Methods
Apply temporal analysis techniques to detect seasonal patterns, long-term trends, and sudden land surface changes.
Python & Jupyter
Use Python-based workflows inside Jupyter Notebooks to query, process, and visualise data cube outputs interactively.
Land Cover Classification
Train and apply machine learning classifiers to map land use and land cover from multi-temporal satellite imagery.
Change Detection
Identify and quantify land degradation, deforestation, and agricultural change using dense satellite time series.