Earth Observation & Weather Data Federation with AI Embeddings

Informations projet

Embed2Scale

N° de convention de subvention: 101131841

DOI

10.3030/101131841

Date de signature de la CE 11 Decembre 2023

Date de début 1 Janvier 2024

Date de fin 31 Decembre 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 2 618 047,50

Contribution de l’UE

€ 2 618 047,50

Coordonné par

MARTEL INNOVATE BV
Netherlands

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (8)

Use Case Implementation Report

This deliverable will describe the implementation features of the Use Cases of the first development phase, also considering the requirements.

Analysis of ARD data and APIs

Report on current state of the ARD and associated data processing APIs

Data access for AI-compressors

Documentation, complete source code and configuration files. Pull Requests on OpenEO and OpenDataCube git repositories.

Reference Code for AI-compressors

Python code as sample reference of the AI-compressor methodologies for Earth observation

Dissemination and Communication Strategy and Plan

This deliverable provides the dissemination and communication foundation, including the strategic guidance to direct Embed2Scale community building activities for broad and durable impact. It will be led by Task 5.1.

SSL4EO-S12-downstream benchmark dataset

Provides an open-source benchmark dataset to assess AI-compressor methodologies

AI-compressor containerization CICD pipeline

Documentation, complete source code and configuration files

Report on State-of-the-Art in AI-compressors

Review on AI-compressors in the light of the Embed2Scale application pillars Embed2{Train,Infer,Transfer,Find}

Other (1)

Benchmarking environment for AI-compressors

Benchmarking environment to evaluate AI-compressor performance available to run an Earth observation community data challenge

Publications

Conference proceedings (2)

Earth Observation Applications through Neural Embedding Compression from Foundation Models

Auteurs: Carlos Gomes, Thomas Brunschwiler
Publié dans: 2024
Éditeur: Copernicus GmbH
DOI: 10.5194/egusphere-egu24-19460

Geospatial Foundation Models for Efficient Retrieval of Remote Sensing Images

Auteurs: Brunschwiler, T., Blumenstiel, B., Moor, V., and Kienzler, R.
Publié dans: 2024
Éditeur: EGU General Assembly 2024
DOI: 10.5194/egusphere-egu24-18300

Other (3)

Scalable Efficient Compression in Large-Scale Earth Observation

Auteurs: Erik Scheurer, Jiangtao Wang, Rocco Sedona, Stefano Maurogiovanni, Benedikt Blumenstiel, Johannes Jakubik, Paolo Fraccaro, Thomas Brunschwiler, Stefan Kesselheim, Gabriele Cavallaro
Publié dans: 2025
Éditeur: Copernicus GmbH
DOI: 10.5194/EGUSPHERE-EGU25-19016

"Neural Embedding Compression for Earth Observation Data – an Ablation Study"

Auteurs: Amelie Koch, Isabelle Wittmann, Carlos Gomez, Rikard Vinge, Michael Marszalek, Conrad Albrecht, Thomas Brunschwiler
Publié dans: 2025
Éditeur: Copernicus GmbH
DOI: 10.5194/EGUSPHERE-EGU25-17172

Earth Observation embeddings at the test: A novel benchmark to evaluate (neural) compression for satellite imagery

Auteurs: Rikard Vinge, Michael L Marszalek, Jannik Schneider, Conrad M Albrecht
Publié dans: 2025
Éditeur: Copernicus GmbH
DOI: 10.5194/EGUSPHERE-EGU25-16756