Open-EO Standard
for Earth Observation Analysis
The openEO API allows users to connect to Earth observation cloud back-ends in a simple and unified way.
One API.
Any cloud. Any language.
Whether you're using Python, R , JavaScript or Julia - openEO lets you write your analysis once and run it on any compatible back-end. No vendor lock-in. No rewriting code.
openEO is not to be confused with independant services that implement the specifications such as CDSE. For a list of services built on top of openEO, please visit the openEO Hub.
Are you interested inβ¦
openEO can be used to process and analyze Earth observation data from diverse sources in a unified and efficient manner.
Quickly build true-color composites from Sentinel-2 and export clean visuals for maps, reports, and monitoring dashboards.
True-color Sentinel-2 composite output.
Best for: EO quicklooks, change communication, report-ready imagery
Data used: Sentinel-2 L2A
Outcome: RGB composite image
Combine spectral bands to derive vegetation and environmental indicators with reusable, cloud-executed openEO pipelines.
Spectral index style output from band combinations.
Best for: index workflows and environmental monitoring
Data used: Sentinel-2 L2A
Outcome: EVI Geotiff image
Inject your domain logic with UDFs to extend standard processes while keeping your workflow portable across back-ends.
Workflow extension pattern for custom UDF logic.
Best for: custom algorithms and domain-specific logic
Data used: Sentinel-2 L2A
Outcome: custom UDF process
Package your workflow as a user-defined process so teams can execute the same analysis at scale with one endpoint.
API-oriented publishing view for reusable services.
Best for: operational teams and repeatable workflows
Data used: Sentinel-2 L2A Outcome: reusable UDP service
Orchestrate many jobs over large regions and time ranges while preserving reproducibility and runtime efficiency.
Batch processing view for multi-job execution.
Best for: regional to continental scale analysis
Data used: Sentinel-2 L2A Outcome: batch job results and summaries
Train and apply machine-learning classifiers directly in your EO workflow to create reproducible land-cover intelligence.
Model training output with classification-ready features.
Best for: classification and model-driven EO analysis
Data used: Sentinel-2 L2A and Sentinel-1 GRD Outcome: trained model and inference maps
Choose your path
New to openEO? Start with a guide for your preferred language or tool.
Explore the openEO Hub
Find an openEO service and start working with Earth observation data.
Concepts & Glossary
Understand datacubes, processes, UDFs, and the openEO data model before writing any code.
Cookbook
Get started with practical examples and step-by-step guides to use openEO effectively for a specific usecase.
Built-in openEO Processes
Learn how to use different openEO processes to analyze Earth observation data efficiently using either π Python, π R or β‘ JavaScript clients.
QGIS Plugin
Access openEO back-ends directly from QGIS with a graphical interface to visualize openEO outputs.
For Developers
Build a back-end or client library. API reference, profiles, and implementation guidelines.
Whatβs new
| Date | Title | Author |
|---|---|---|
| May 14, 2026 | OGC publishes openEO as a new Community Standard | |
| Feb 3, 2026 | openEO API 1.3.0 and openEO Processes 2.0.0 RC2 released | |
| Dec 22, 2025 | New openEO QGIS plugin has been released |