embed2scale/TerraCodec-1.0-TT-S2L1C

TerraCodec TT – Sentinel-2 L1C

Neural Compression for Earth Observation

TerraCodec (TEC) is a family of pretrained neural compression codecs for multispectral Sentinel-2 satellite imagery. The models compress optical Earth observation data using learned latent representations and entropy coding.

This repository provides Temporal Transformer (TEC-TT) models trained on Sentinel-2 L1C imagery. The main TerraCodec models are released for Sentinel-2 L2A data, the L1C variants were used for declouding experiments in the paper.

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Model Architecture

This repository contains the TEC-TT (Temporal Transformer) variants of TerraCodec for S2L1C data.

TEC-TT extends the TerraCodec image codecs by modeling temporal dependencies across satellite image sequences. Each frame is first encoded using an ELIC-style CNN encoder–decoder to obtain latent representations. A temporal transformer then predicts the probability distribution of the current frame’s latents conditioned on previously encoded frames.

By exploiting redundancy across seasonal observations, TEC-TT achieves improved compression efficiency for multi-temporal satellite imagery.

See the paper for additional architectural and training details.

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Input Format

Codec type	Expected shape	Example
Temporal codecs	[B, T, C, H, W]	[1, 4, 13, 256, 256]

• Inputs use 13 Sentinel-2 L1C spectral bands
• Recommended spatial size: 256 × 256
• Models were trained on four seasonal frames, but can process any number of timesteps during inference (higher T increases compute)

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Normalization

Models were trained on Sentinel-2 L1C imagery.

Inputs should be standardized per spectral band using dataset statistics:

mean = torch.tensor([1607.345, 1393.068, 1320.225, 1373.963, 1562.536, 2110.071, 2392.832, 2321.154, 2583.77, 838.712, 21.753, 2205.112, 1545.798])

std = torch.tensor([786.523, 849.702, 875.318, 1143.578, 1126.248, 1161.98, 1273.505, 1246.79, 1342.755, 576.795, 45.626, 1340.347, 1145.036])

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Usage

Install TerraCodec:

pip install terracodec

Load pretrained models:

from terracodec import terracodec_v1_tt_s2l1c

model = terracodec_v1_tt_s2l1c(
    pretrained=True,
    compression=5
)

# Fast reconstruction (no bitstream)
reconstruction = model(inputs)

# True compression
compressed = model.compress(inputs)
reconstruction = model.decompress(**compressed)

Feedback

If you have questions, encounter issues or want to discuss improvements:

• open an issue or discussion on GitHub
• or contribute directly to the repository

GitHub repository: https://github.com/IBM/TerraCodec

Citation

If you use TerraCodec in your research, please cite:

@article{terracodec2025,
  title   = {TerraCodec: Neural Codecs for Earth Observation},
  author  = {Costa Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt},
  journal = {arXiv preprint arXiv:2510.12670},
  year    = {2025}
}