Introduction

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Introduction#

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

🎯 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.

📚 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.

👥 Target Audience#

This demonstration is aimed at:

  • Earth Observation scientists

  • Geospatial data engineers and analysts

  • Researchers seeking scalable, hierarchical solutions for large EO datasets

🧭 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

🚀 Let’s get started with the workflow!

Contents