Focus: Developing, testing, and prototyping Earth Observation workflows.
This use case centers on creating new methods for processing geospatial data. Users explore different techniques, write and test code, and visualize intermediate results to better understand patterns and relationships in EO data. The emphasis is on experimentation, flexibility, and rapid iteration.
This phase is particularly relevant for data scientists and researchers aiming to experiment with new techniques, machine learning models, or domain-specific indices. During this phase, the focus is not yet on scale or automation, but on designing something effective and accurate.
🛠️ Workspace tools:
JupyterLab provides an interactive environment for exploring data, developing algorithms, and testing processing steps in notebooks.
Conda Store creates reproducible software environments and helps ensure that an algorithm uses consistent dependency versions.
File Browser provides access to input data, notebooks, scripts, and generated files stored within the Workspace.
Credentials Manager securely provides credentials for protected data sources, APIs, cloud storage, and other external services.
Container Registry stores versioned container images when an algorithm is ready to be packaged for reproducible execution.