Hugging Face model: geotessera/TESSERA-V-2.0-2B-Teacher
Author: geotessera
Created: 2026-07-24 15:50:45
Last modified: 2026-07-24 16:24:27
Library: pytorch
Likes: 0 | Downloads: 0
Tags: pytorch, geospatial, earth-observation, remote-sensing, foundation-model, sentinel-1, sentinel-2, sar, multispectral, temporal, pixel-embedding, self-supervised-learning, barlow-twins, teacher-model, en, arxiv:2607.03949, license:cc0-1.0, region:us
Card metadata:
language: en
license: cc0-1.0
license_name: cc0
license_link: https://creativecommons.org/publicdomain/zero/1.0/
library_name: pytorch
provider: University of Cambridge / ucam-eo
funder: UKRI MR/Z505456/1; AIRR DAWN; AMD; Vultr; AIRR Isambard AI; STFC Durham DiRAC HPC Facility; Microsoft AI For Good Lab; Tarides; Jane Street; Google; Dr. Robert Sansom; John Bernstein
tags: geospatial, earth-observation, remote-sensing, foundation-model, sentinel-1, sentinel-2, sar, multispectral, temporal, pixel-embedding, self-supervised-learning, barlow-twins, teacher-model
embedding_spatial_types: pixel
embedding_temporal_type: multi-date
embedding_spatial_context: spatial context determined by embedding spatial type
embedding_temporal_context: annual multi-temporal context from valid Sentinel-1 and Sentinel-2 observations for the same pixel
embedding_dimension: 1024
description: TESSERA v2 2B teacher is a 2.06-billion-parameter pixel-wise geospatial foundation encoder for multimodal Sentinel-1/2 time series, producing a 1024-dimensional per-pixel representation. It is the large-capacity model from which the compact TESSERA v2 students are distilled, and is released primarily as a distillation target and research artifact rather than as a deployment model.
compression: None. Output is fp32 1024-d. For deployment-oriented compression (128-d Matryoshka output, int8 quantization) use the distilled TESSERA v2 students instead.
intention: Distillation of compact student encoders, and scientific research into large pixel-wise Earth-observation representation models. Downstream analysis is possible but the distilled students are the intended deployment path.
cautions: Inference with this model is expensive: 2.06B parameters are evaluated per pixel, so encoding at tile or global scale is impractical on ordinary hardware and a GPU is effectively required. For any large-scale or production embedding generation, use the distilled TESSERA v2 students, which reach comparable downstream quality at a small fraction of the cost. The Sentinel-2 input channel order is NOT the conventional ascending-wavelength order, and Sentinel-1 uses POOLED statistics over a merged ascending+descending stream -- both differ from naive expectations and from the per-source convention used elsewhere; see the Input contract section. TESSERA representations are annual, 10m spectral-temporal features, not raw imagery and not real-time monitoring data.
precomputed_embeddings: no
publication_link: https://arxiv.org/abs/2607.03949
model_architecture: Two per-modality backbones (Sentinel-2, and a single merged Sentinel-1 stream), each an MLP band embedding plus sinusoidal day-of-year positional encoding, a 4-layer pre-LN Transformer encoder (d_model=4096, 4 heads, FFN=16384, ReLU, QK-norm enabled) and single-head softmax attention pooling over time. The two pooled modality tokens receive a learnable modality embedding and pass through a 2-layer fusion Transformer (d_model=4096, FFN=8192), are concatenated to 8192-d, and are reduced by an MLP dim_reducer (8192 -> 16384 -> 1024) ending in a non-affine LayerNorm.
pretraining: data_types: multispectral, SAR
product_names: Sentinel-2 L2A, Sentinel-1 RTC
training_strategy: Self-supervised Barlow Twins over per-pixel annual Sentinel-1/2 time series.
patch_size: 1
temporal_context: annual multi-date Sentinel-1/2 time series
inference: data_types: multispectral, SAR
product_names: Sentinel-2 L2A, Sentinel-1 RTC
patch_size: 1
temporal_context: annual multi-date time series; every valid observation per pixel is used and the observation count is bucketized to the nearest bin in 8, 16, ..., 256