Earth observation framework for scaled-up processing in Python
Earth observation framework for scaled-up processing in Python.
Analyzing Earth Observation (EO) data is complex and solutions often require custom tailored algorithms. In the EO domain most problems come with an additional challenge: How do we apply the solution on a larger scale?
Working with EO data is made easy by the eo-learn package, while the eo-grow package takes care of running the solutions at a large scale. In eo-grow an EOWorkflow based solution is wrapped in a pipeline object, which takes care of parametrization, logging, storage, multi-processing, EOPatch management and more. However pipelines are not necessarily bound to EOWorkflow execution and can be used for other tasks such as training ML models.
Features of eo-grow include:
- Direct use of
EOWorkflowprocedures - Parametrizing workflows by using validated configuration