This course explains the open formats that let you find Earth observation data and read only what you need from object storage: STAC, Cloud Optimized GeoTIFF, Zarr and GeoParquet. Animated infographics and four hands-on notebooks on East Attica support all learners.

Course description

The catalogue already teaches how to run cloud infrastructure, with virtual machines and Kubernetes, and how to process Earth observation data on specific platforms. This course explains the open data formats that make large-scale analysis efficient on any cloud.

You will see why the workflow of downloading first and processing afterwards breaks down at petabyte scale, and how cloud native formats let a program read only what it needs over HTTP. You will find imagery with the SpatioTemporal Asset Catalog (STAC), both as a static catalogue of linked JSON files and through a search API; stream rasters from Cloud Optimized GeoTIFFs (COG); build and chunk multidimensional cubes in Zarr; and query millions of vector features in GeoParquet with DuckDB.

In four short notebooks and a capstone you build a small time series for East Attica in Greece by streaming Sentinel-2 data from a public STAC catalogue, without downloading a single full scene, and follow the burn scar of the August 2024 wildfire. Every notebook counts the bytes it moves, runs on a laptop or a cloud virtual machine, needs no account and no key, and comes with an offline copy of its inputs.

8 video lessons · 51 min of video · 4 hands-on notebooks · 8 quizzes · capstone

What you will learn

  • Finding data with STAC

    Explain the two components of STAC, the required metadata specification and the optional API specification, and when a static catalogue is enough. Read STAC items and assets, and search a public STAC API by area, date and cloud cover from Python.

  • Rasters and data cubes

    Explain how a Cloud Optimized GeoTIFF is organised, check one, and stream windows and overviews with rasterio. Stack many scenes into a lazy, cloud-masked data cube with odc-stac, store cubes in Zarr version 3, and choose a chunk layout for maps or for time series.

  • Vector data and the full workflow

    Query GeoParquet on object storage with DuckDB and GeoPandas, using column selection and bounding box filters. Combine STAC, COG, Zarr and GeoParquet in one workflow, and choose the right format for a dataset and a task.

Course curriculum

  1. 01

    • 1.1 From downloading to streaming

    • Quiz 1.1

    • Course materials: notebooks, data and setup guide
  2. 02

    • 2.1 STAC concepts

    • Quiz 2.1

    • 2.2 Searching with STAC APIs

    • Quiz 2.2

    • Hands-on 1: STAC search for East Attica
  3. 03

    • 3.1 Inside a Cloud Optimized GeoTIFF

    • Quiz 3.1

    • 3.2 Streaming COGs in Python

    • Quiz 3.2

    • Hands-on 2: Streaming a COG time series

    • 3.3 Zarr data cubes

    • Quiz 3.3

    • Hands-on 3: Writing and reading a Zarr cube
  4. 04

    • 4.1 GeoParquet for large vector data

    • Quiz 4.1

    • Hands-on 4: Querying GeoParquet with DuckDB

    • 4.2 Putting it together: an East Attica time series

    • Quiz 4.2

    • Capstone: an East Attica time series with all four formats

Who is it for

  • EO and GIS practitioners, who work with satellite imagery and vector data and want faster, lighter workflows. You follow every lab with basic Python and a laptop with about 8 GB of RAM; no account, no API key and no GPU.

  • Developers and data engineers, who build geospatial pipelines. Optional deep dive slides explain the internals of each format.

  • Managers and decision makers, who want to understand what these formats change in cost and workflow. The concept part of each lesson needs no code.

Instructor

Dr. Maria Makaronidou is a Forest Engineer and Environmental Scientist who earned her PhD in Environmental Science from Lancaster University (2020), focusing on the local climate effects of solar parks. She also holds an Integrated Master in Forestry and Natural Environment from the Aristotle University of Thessaloniki (AUTh), specialising in forest infrastructure and ArcGIS. Currently, she serves as an AI-Learning Design and Strategy Consultant at GOVADI, developing geospatial courses for the geo.university platform. Her extensive academic career includes serving as an Associate Lecturer and Tutor at Lancaster University (2015–2019), where she led along with class lectures, field research in Mediterranean ecosystems and workshops on energy metrics. From 2025 to the present, she continues her teaching as an Adjunct Lecturer at AUTh, instructing courses on Snow Science, Hydraulics, and Environmental Geotechnical works. Simultaneously, she is a Scientific Associate at the Athena Research Center, managing European projects like SciLake and EOSC-EVERSE. Her expertise spans Agrivoltaics, Environmental Impact Assessments (EIA), and the application of AI and Big Data in environmental configurations. Further, Maria is a member of the EOSC expert group and the Geotechnical Chamber of Greece. Her research is widely published, covering microclimatic variability and the ecological footprint of infrastructure.

Maria Makaronidou

Senior Instructor

Use of AI tools

Transparency notice regarding the course content creation

The academic concepts and slides for this course were developed by our lecturer. The narration was co-authored using AI tools and subsequently edited. The narration is provided by a synthetic AI voice.