Community notebooks and code snippets for the openEO Python client, organised by intent so you can jump straight to the kind of workflow you want to learn.
Each numbered folder groups notebooks by what the user is trying to do, not by data source. Read the section README for a full notebook-by-notebook breakdown.
| Folder | What lives here |
|---|---|
| 10_getting-started | Start here. Your first openEO connection, authentication, a datacube, and a download. |
| 20_data-discovery | Access existing products (MODIS, PROBA-V, CLMS, WorldCereal, Global Flood Monitoring, external STAC catalogs). |
| 30_openeo-processing | Produce and transform datacubes: composites, masks, merges, UDFs, UDPs, terrain, gap-filling. |
| 40_machine-learning | Train and apply ML models (Random Forest, ONNX, U-Net) inside openEO via UDFs. |
| 50_thematic-notebooks | End-to-end use cases (floods, fires, heatwaves, droughts, land cover, soil moisture, …). |
| 60_geospatial-embeddings | Compression and representation learning workflows (CORSA, PCA, TESSERA). |
| 70_platform-and-large-scale | Federation, batch orchestration, and monitoring across backends. |
- If a notebook mainly shows how to get a ready-made product into a datacube, it belongs in
20_data-discovery. - If it mainly shows how to build something new with openEO processes, it belongs in
30_openeo-processing. - If it wraps a full applied use case (data + processing + interpretation), it belongs in
50_thematic-notebooks. - ML training/inference workflows belong in
40_machine-learningeven if they also touch discovery or processing.