EUMETSAT
The European Organisation for the Exploitation of Meteorological Satellites
- more details to follow
IBM
Workshop 1: Introduction to EO Foundation Models: Inference and Fine‑Tuning
This workshop introduces participants to Earth Observation foundation models and provides hands‑on experience running inference and performing a lightweight fine‑tuning workflow. Attendees will work with Prithvi and TerraMind to explore their capabilities, compare outputs, and adapt a model to a simple downstream task. The session builds practical intuition for how EO foundation models generalise and how fine‑tuning improves task‑specific performance.
Learning Outcomes
- Understand EO foundation model concepts
- Run inference with Prithvi and TerraMind
- Perform a simple fine‑tuning workflow
Prerequisites
- Google account for Colab, or a laptop with local dependencies installed
- Basic Python (variables, functions, simple scripts)
- Some familiarity with Jupyter/Colab notebooks
- Exposure to remote sensing concepts (bands, resolution) and geospatial machine learning (data formats, error metrics) is helpful but not required
IBM
Workshop 2: Advanced Geospatial AI: Multimodality, TerraTorch Iterate, and TerraKit (1.5 hrs)
This advanced session expands into multimodal geospatial AI and expert workflows with TerraMind. Participants will experiment with combining inputs (e.g. optical + DEM), explore how modality choices affect performance, and learn how to use TerraTorch Iterate for hyper-parameter optimisation. The workshop also introduces TerraKit for dataset preparation, enabling participants to design and execute more sophisticated geospatial AI pipelines.
Learning outcomes
- Understand and run advanced TerraMind multimodal fine-tuning and inference
- Use TerraTorch Iterate for performance optimisation
Prepare fine-tuning data with Terrakit
Prerequisites
- Google account for Colab, or a laptop with local dependencies installed
- Completion of Workshop 1 or equivalent experience
Comfortable with Python and notebook‑based workflows
Basic understanding of EO data types (optical vs SAR, DEMs, metadata)
NEODAAS
Workshop 1: Introduction to AI for EO data
Learn how machine learning can unlock insights from Earth Observation data. In this introductory workshop, we’ll break down the key steps in preparing EO data for ML models and explore how different problem types—classification, regression, and segmentation—apply to real environmental challenges. Using Marine Primary Production as our case study, we’ll walk through how to design meaningful ML workflows and demonstrate a practical example using a simple Random Forests model.
Learning Outcomes
- Have an awareness of best practices around pre-processing data for machine learning, including sampling, normalisation, and splitting.
- Understand the difference between classification, regression, and segmentation problems.
Prerequisites
- none
NEODAAS
Workshop 2: more advanced workshop on AI and EO
- more details to come
PlotToSat Workshop
Hosted by Milto Miltiadou
This is a practical workshop on how to use PlotToSat for efficiently generating monthly aggregated Sentinel-1 and Sentinel-2 time-series across distributed field-plot regions and polygons defined in a CSV file or Shapefile, respectively.
Associated paper
Github: https://github.com/Art-n-MathS/PlotToSat
BlueSky: @plottosat.bsky.social
Prerequisites
- PlotToSat uses the cloud platform Google Earth Engine and having an account (https://earthengine.google.com/signup) and VS code installed (https://code.visualstudio.com/) are the prerequisites.
- more details to follow
Where your models can be trusted:
evaluating spatial machine learning reliably (with examples in R)
Hosted by Jakub Nowsad
Understanding how well spatial predictions perform and where they can be trusted requires proper validation and awareness of the model’s spatial limits.
This workshop introduces methods for robust evaluation, including kNN distance matching cross-validation and the Area of Applicability (AoA), using examples in R with the CAST package.
Participants will learn to interpret spatial model performance realistically and apply these concepts broadly, beyond R.
The session combines demonstrations, optional hands-on exercises, and discussion, giving participants both practical skills and conceptual understanding.
Learning Outcomes
- Evaluate spatial predictions using kNN distance matching CV and AoA
- Understand and interpret where predictions are reliable
- Apply these evaluation concepts to their own workflows
Prerequisites
- Basic familiarity with machine learning and spatial data concepts
- Some experience with R is helpful but not required
- Participants who want to follow the examples should have R and an R IDE (e.g., RStudio, Positron, etc.) installed, along with the CAST package
ML4EO 