TESSERA: precomputed FAIR global pixel embeddings for earth representation and analysis
Event date: 2025-12-11
Event location:
Welcome to this week’s Learning Machines seminar.
This seminar is a collaboration between RISE and Climate AI Nordics – climateainordics.com.
Title: TESSERA: precomputed FAIR global pixel embeddings for earth representation and analysis
Speaker: Zhengpeng (Frank) Feng, Cambridge
Abstract: Petabytes of satellite Earth Observation (EO) data are freely available and can address critical global challenges. However, EO data quality is poor due to clouds and variable lighting conditions. To address this, practitioners typically use compositing, but this critically removes the temporal phenological signal. Moreover, supervised machine learning to map composited pixels to task-specific classes requires accurately labelled data that are rarely available. We present TESSERA, a pixel-oriented foundation model for EO time series that creates 128-dimensional latent embeddings requiring only a few labels for task-specific training to achieve state-of-the-art performance across diverse complex tasks. TESSERA uses two encoders that combine optical data with synthetic aperture radar backscatter coefficients at 10m resolution, creating embeddings fused with a multilayer perceptron to generate annual global embedding maps. TESSERA closely matches or outperforms state-of-the-art task-specific models and other foundation models across five diverse downstream tasks. It is unprecedented in ease of use, scale, and accuracy: no other open foundation model provides precomputed outputs with global, annual coverage at 10m resolution.
About the speaker: Frank Feng is a second-year Ph.D. student in the Department of Computer Science and Technology at the University of Cambridge. His research interests lie at the intersection of machine learning and earth sciences, with a particular focus on developing self-supervised learning algorithms in remote sensing.
Location: This is an online seminar. Connect using Zoom.
Date: 2025-12-11 15:00
Upcoming seminars:
- 2026-01-22: Nita Mulliqi, Karolinska Institute
- 2026-02-05: Pablo Villanueva Perez, Lund University
- 2026-03-05: Purnendu Sardar, Lund University
- All seminars are 15:00 CET.
More information and coming seminars: https://ri.se/lm-sem
– The Learning Machines Team