TESSERA: Programming Petabytes of Earth Observations using Foundation Models by Prof. Anil Madhavapeddy

Venue. Bharti 501

Abstract. We present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) earth observation time series that learns robust, label-efficient embeddings.  Our goal with TESSERA is to make manipulating global satellite intelligence as easy as LLMs did for natural language! Towards this we release global, annual, 10m, pixel-wise embeddings together with open weights and code and lightweight adaptation heads, providing practical tooling for large-scale retrieval and inference at planetary scale.  As with any good foundation model, there are a staggering array of downstream tasks which can benefit. TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency across diverse classification, segmentation, and regression tasks.

In this talk, I’ll take you through an array of problems our users are applying it to, ranging from the ecological to the urban to the temporal. By the end of the talk, we aim to have you identify a seemingly impossible spatial problem that is now within range to solve yourself using our easy-to-install Python package, geotessera. Bring your favourite coding agents!

Speaker: Anil Madhavapeddy, Professor of Planetary Computing, University of Cambridge