Open-Earth-Monitor Global Workshop 2026

Satellite Earth Observation (EO) time series are fundamental to monitoring our planet's changing environment. However, inconsistent revisit times and frequent cloud obstruction in optical data (Sentinel-2) often force practitioners to rely on lossy data compositing, which discards critical phenological information.
In this keynote, we introduce TESSERA (Temporal Embeddings of Surface Spectra for Earth Representation and Analysis), a pixel-wise foundation model designed to overcome these challenges. TESSERA leverages multi-modal fusion of Sentinel-1 (radar) and Sentinel-2 (optical) data, employing a self-supervised learning framework based on Barlow Twins and random temporal sampling. This approach ensures high robustness to irregular sampling and missing data without requiring expensive ground-truth labels.
A key highlight of TESSERA is its scale and commitment to Open Science: trained on a global dataset spanning 2017–2025, the model provides high-dimensional temporal embeddings that capture the "spectral fingerprint" of the Earth's surface. In alignment with the FAIR principles, we are committed to making TESSERA an open-access resource for the community. We will demonstrate how TESSERA achieves state-of-the-art performance in downstream tasks such as crop type mapping and land cover classification with minimal labeled data, paving the way for the next generation of open-source, distributed GeoAI monitoring systems.

GeoFoundation embeddings encode huge amounts of Earth Observation data and by condensing this into a small vector of numbers, they can make many downstream analyses much easier to perform. However, the embeddings represent a latent state and as such can be abstract to understand.
This workshop aims to demonstrate how embeddings can be used and explore how to visualize them and make them more usable.