EarthCODE Documentation

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Deep Earth System Data Laboratory (DeepESDL) ​

🛠️ Page Under Development Content is being actively developed and updated

for this page. EarthCODE's documentation is a living document and will be continuously updated with detailed reviews.

Found out more at: earthsystemdatalab.net/

About ​

Deep Earth System Data Laboratory, a platform providing analysis-ready data cubes in a powerful virtual environment for the Earth Science research community. DeepESDL offers a full suite of services to facilitate data exploitation, share data and source code, and publish results. Special emphasis is placed on supporting machine learning and artificial intelligence approaches, including preparation of AI-ready datasets, integrated programming environments, and scalable processing resources.

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Developing Workflows ​

Learn By Example

To help you get hands-on experience with developing workflows in DeepESDL, check out the Permafrost example in the cube-gen repository on GitHub.

Developing Workflows

Developing workflows in DeepESDL involves building reproducible pipelines for scientific analysis on large-scale Earth observation (EO) data. Workflows are typically authored as Jupyter notebooks that combine Python code, documentation, and visualization. They make use of the DeepESDL data cubes (stored in Zarr format and accessed via xcube and xarray) and are designed to follow open standards such as STAC for metadata and provenance.

A workflow usually consists of:

  • Data access through the xcube stores.

  • Data transformation using xarray operations and xcube utilities.

  • Analysis steps that apply algorithms or models to EO datasets.

  • Visualization using xcube viewer or Python libraries for interactive exploration and results presentation.

By structuring workflows this way, users can convert exploratory notebooks into reproducible analysis packages.

Publishing to EarthCODE

Publishing to EarthCODE within DeepESDL is possible with deep-code. deep-code is a lightweight Python tool for publishing datasets and scientific workflows from DeepESDL directly to the EarthCODE open-science-catalog. It provides both a command-line interface (CLI) and a Python API for flexible use.

See the DeepESDL documentation for detailed information.

Data Access ​

DeepESDL provides access to a broad range of Earth System datasets through a unified, analysis-ready data cube framework with xcube.

Supported sources include:

An expanded overview of supported data sources is provided here.

Specialised Hardware & Services ​

CPU, GPU, Dask clusters, MLflow, TensorFlow, ml4xcube, geodb, xcube Viewer

Visualization Tools ​

DeepESDL provides several visualization options designed to make multi-dimensional Earth system data cubes more accessible, moving beyond standard Python plotting libraries.

xcube Viewer ​

DeepESDL integrates the xcube Viewer, a web-based tool for interactive browsing and inspection of data cubes.

  • It allows users to navigate data across space, time, and variables.
  • The viewer can also be embedded directly in Jupyter Notebooks, enabling interactive exploration alongside code and analysis.
  • A public deployment at viewer.earthsystemdatalab.net provides access to shared datasets without requiring additional setup.
  • For more information see the dedicated documentation.

Lexcube ​

Lexcube is available both as a Jupyter Notebook integration and as a standalone web application.

  • Inside notebooks, it offers lightweight interactive visualization of data layers inline with the workflow.
  • As a web app, Lexcube enables direct data cube inspection through a browser interface, useful for sharing results beyond the notebook environment.
  • For more information see the documentation.

4D Viewer ​

The 4D Viewer is a standalone tool for exploring the full dimensionality of Earth system data, including time and vertical axes.

  • The 4D Viewer is suited for comprehensive analysis of highly complex datasets where multiple dimensions need to be visualized simultaneously.
  • For more information see the documentation.

DeepESDL also supports dissemination of datasets via public xcube Viewer applications, ensuring that published data products are accessible and explorable by the wider community.

Right Sizing and Network of Resources ​

See Network of Resources for details on access through ESA’s supporting programmes or this overview.

Tutorials ​

Support and Communities ​

For more support write an email to esdl-support@brockmann-consult.de.
Community discussions and updates are available via the DeepESDL website.

Full Documentation Can be Found At ​

https://earthsystemdatalab.net/guide/

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