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Embedding workflows for Earth Observation tasks

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    • Date
    25 February 2026
    Timeframe
    14:00 - 15:30 CET Geneva
    Duration
    90 minutes
    • Days
      Hours
      Min
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    Efficient data handling is essential for managing and unlocking the growing volume of Earth Observation (EO) archives. Recent advances in machine learning enable neural embeddings, that is: compact, meaningful representations to distill information into small vectors for downstream tasks and near real-time applications.

    This workshop demonstrates how modern Foundation Models can generate EO embeddings that preserve task-relevant information. These embeddings allow lightweight decoders up to two orders of magnitude smaller, accelerating training and inference.

    In two segments, participants will:

    – Gain hands-on experience with TerraTorch, an open-source library offering tools to extract embeddings via a no-code CLI, adjust aggregation levels for different use cases, visualize and analyze embedding properties and run complete workflows for downstream tasks.

    – Be introduced to NeuCo-Bench, a benchmarking framework from the Embed2Scale consortium. NeuCo-Bench enables rapid evaluation of embeddings across diverse tasks within seconds, requiring no prior setup.

    The workshop will conclude by integrating TerraTorch and NeuCo-Bench in a full workflow, showcasing instant evaluation of generated embeddings. It highlights efficiency gains, demonstrates practical workflows, and shows how embeddings drastically reduce computation and training time, with interactive demos ensuring participants leave with actionable skills for EO tasks.

    Session Objectives:
    By the end of this session, participants will be able to:

    • Get an introduction to neural embeddings and their role in modern EO workflows.
    • Learn to extract EO embeddings for your data using TerraTorch’s no-/low-code CLI.
    • Benchmark embedding methods within seconds using NeuCo-Bench.
    • Build your own embedding downstream task workflows and integrate NeuCo-Bench evaluations into them.

    Recommended Mastery Level / Prerequisites:
    Mastery Level
    Intermediate. Participants should have some familiarity with Earth Observation workflows and basic Python usage. A general understanding of machine learning concepts is helpful but not mandatory.

    Prerequisites

    General Knowledge

    • Basic familiarity with Earth Observation data and common formats.
    • A general understanding of machine learning concepts is helpful but not required.

    Technical Skills

    • Comfortable using a terminal and running simple commands.
    • Basic Python familiarity is needed for the hands-on segments.

    Environment

    • A laptop capable of running Python locally or access to Google Colab.
    • Ability to open and run lightweight scripts/ notebooks.
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