Hugging Face model: geotessera/TESSERA-V-1.0
Author: geotessera
Created: 2026-05-22 16:24:04
Last modified: 2026-05-22 16:54:21
Library: pytorch
Likes: 3 | 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, qat, int8, en, 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, qat, int8
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: 128
description: TESSERA v1.0 is a pixel-wise geospatial foundation encoder for multimodal Sentinel-1/2 time series. It produces task-agnostic temporal surface-spectrum embeddings for downstream Earth observation analysis. 
compression: Quantization-aware training is used for the public embedding product. Precomputed embeddings are stored as int8 arrays plus float32 per-pixel scale maps; fp32 embeddings can be reconstructed as int8.astype(float32) * scales[..., None].
intention: Scientific research and downstream geospatial analysis, including environmental monitoring, conservation, habitat mapping, carbon and biomass estimation, agricultural monitoring, crop mapping, food security analysis, climate change research, and sustainable land-use applications.
cautions: TESSERA embeddings are annual, 10m spectral-temporal representations, not raw imagery and not real-time monitoring data. Accuracy may degrade in regions or years with very few valid observations, and users should validate performance for their own task and geography.
precomputed_embeddings: yes: https://github.com/ucam-eo/geotessera
publication_link: https://arxiv.org/abs/2506.20380
model_architecture: Dual-branch Sentinel-2/Sentinel-1 temporal encoder with learnable day-of-year positional encodings, 4-layer Transformer encoders, temporal pooling, modality fusion, and an MLP dim_reducer. v1.1 uses concat fusion, latent_dim=192, representation_dim=192, save_embedding_dim=128, and a merged Sentinel-1 stream.
pretraining: data_types: multispectral, SAR
product_names: Sentinel-2 L2A, Sentinel-1 RTC
training_strategy: Self-supervised Barlow Twins with sparse temporal sampling, global data shuffling, and mix-based regularization. Note: paper-reported benchmark details are for TESSERA v1, while this card describes the released TESSERA v1.1 encoder.
training_resource: 8x8 AMD MI300X
spatial_extent: Global random sampling; 2.1 billion dpixels in total.
temporal_extent: 2017-2025
patch_size: 1
temporal_context: annual multi-date Sentinel-1/2 time series
batch_size: 65536
inference: data_types: multispectral, SAR
product_names: MPC checkpoint: Microsoft Planetary Computer Sentinel-2 L2A + Sentinel-1 RTC, AWS checkpoint: Earth-search Sentinel-2 L2A COGs + ASF OPERA RTC-S1
patch_size: 1
temporal_context: annual multi-date time series.