Abstract
This hands-on training aims to provide participants with practical experience in processing Earth Observation (EO) data using openEO. By the end of the session, participants will be able to:
- Understand the core concepts of EO data cubes and cloud-native processing
- Transition from local data processing to cloud-based environments efficiently, always using the openEO API
- Use openEO Platform (openeo.cloud) to process EO data via multiple cloud providers
- Gain familiarity with Python data access and processing using the openEO API
Training Content & Agenda
Introduction & Overview
- Introduction to the openEO API: functionalities and benefits
- Data cubes concepts and documentation review
- Overview of the "Cubes & Clouds" online course by Eurac Research
Transitioning to Cloud Processing
- Challenges and advantages of moving from local processing to cloud environments
- Overview of cloud providers (VITO Terrascope, EODC, SentinelHub) and their integration with openEO Platform
- Key concepts of FAIR (Findable, Accessible, Interoperable, Reusable) principles implemented by openEO
- STAC: how the SpatioTemporal Asset Catalog allows interoperability
Hands-On Training with openEO
- Setting Up the Environment
-- Accessing openEO Platform JupyterLab instance
-- Clone GitHub repositories for training materials
- Basic openEO Workflow
-- Discovering and accessing EO datasets
-- Executing simple queries using openEO Python Client
-- Processing workflows using local and cloud-based computation
- Multi-Cloud Processing
-- Sample workflow using multiple cloud providers
- Executing an End-to-End EO Workflow
-- Data discovery and preprocessing
-- Applying processing functions (e.g., time-series analysis, indices computation)
-- Exporting and sharing results according to open science principles
Q&A and Wrap-Up
- Discussion on best practices and troubleshooting common issues
- Resources for further learning (EO College, openEO documentation)