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# Introduction

**🌍 Living Planet Symposium 2025**  
**📌 Session C.01.25 DEMO — DGGS: Scalable Geospatial Data Processing for Earth Observation**

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## 🎯 Objective

This demonstration introduces the **Discrete Global Grid System (DGGS)** framework — a powerful approach to efficiently process and analyze large-scale Earth Observation (EO) datasets.

DGGS enables seamless multi-resolution analysis by leveraging hierarchical grid structures. Combined with modern data formats like **Zarr**, it significantly improves data accessibility, storage efficiency, and analytical scalability.

You'll also discover how to interact with DGGS/Zarr data using our open-source Python tool: **`xdggs`**.

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## 📚 What You'll Find in This Demo

- **🔹 Introduction to DGGS**  
  A conceptual overview of DGGS and its benefits for scalable geospatial workflows.

- **🔹 Interactive Exploration with `xdggs`**  
  Learn how to explore, analyze, and visualize HEALPix data using the Python package `xdggs`.

- **🔹 EO Data Example**  
  A practical example showing how to exploire Sentinel-2 Level 2A data in HEALPix.  
  

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## 👥 Target Audience

This demonstration is aimed at:

- Earth Observation scientists  
- Geospatial data engineers and analysts  
- Researchers seeking scalable, hierarchical solutions for large EO datasets


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## 🧭 Format

You’ll be guided through the key features of DGGS using:

- Clear markdown explanations  
- Step-by-step Python code examples  
- Live visualizations  

A short **Q&A session** will follow to discuss use cases, tools, and future development.


If you want to tryout, you can create jupyter lab enviroment using following command

```
git clone https://github.com/EOPF-DGGS/LPS25_demo.git
micromamba env create -n xdggs_demo environment.yml
micromamba activate xdggs_demo
jupyter lab
```

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🚀 **Let’s get started with the workflow!**
