terracodec 0.3.1

Learned compression models for multispectral and multitemporal Earth Observation data.

TerraCodec

Compressing Optical Earth Observation Data

TerraCodec (TEC) is a family of pretrained neural compression models for optical Sentinel-2 Earth Observation imagery. Models compress multispectral images and seasonal time series using learned latent representations and entropy coding.

Compared to classical codecs (JPEG2000, WebP, HEVC), TerraCodec achieves 3–10× higher compression at comparable reconstruction quality on multispectral EO imagery. Temporal models further improve compression by exploiting redundancy across seasonal sequences.

📄 Paper: https://arxiv.org/abs/2510.12670
🤗 Models: https://huggingface.co/embed2scale

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Installation

pip install terracodec

Requirements: Python ≥ 3.10, PyTorch ≥ 2.0

All pretrained checkpoints are automatically downloaded from HuggingFace on first use.

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Models

TerraCodec includes image codecs and temporal codecs for EO data.

Image Codecs

Model	Description
TEC-FP	Factorized-prior model. Smallest, strong baseline.
TEC-ELIC	Enhanced entropy model with spatial + channel context. Better rate–distortion, slightly larger.

TEC Image Architecture

Temporal Codecs

Model	Description
TEC-TT	Temporal Transformer for multispectral time series data. Predicts latent distributions from previous frames.
FlexTEC	Flexible-rate extension of TEC-TT. One checkpoint covers many compression levels via latent repacking and token prediction.

TEC-TT Architecture

FlexTEC Examples

One model, multiple quality levels: by varying the token budget at inference, FlexTEC provides different compression/quality trade-offs. Early tokens encode global structure; additional tokens progressively refine details.

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Pretrained Checkpoints

Image Compression

Checkpoint	Architecture	Training Data	λ values
terracodec_v1_fp_s2l2a	TEC-FP	Sentinel-2 L2A	0.5, 2, 10, 40, 200
terracodec_v1_elic_s2l2a	TEC-ELIC	Sentinel-2 L2A	0.5, 2, 10, 40, 200

Low λ → higher compression. High λ → higher quality.

Temporal Compression

Checkpoint	Architecture	Training Data	λ values
terracodec_v1_tt_s2l2a	TEC-TT	Sentinel-2 L2A (seasonal)	0.4, 1, 5, 20, 100, 200, 700
terracodec_v1_tt_s2l1c	TEC-TT	Sentinel-2 L1C	5, 20, 100

The L1C model was used for the declouding experiments in the paper.

Flexible-Rate

Checkpoint	Architecture	Quality range
flextec_v1_s2l2a	FlexTEC	1–16 (low = high compression)

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Loading Models

Standalone Usage

Image codec — pass compression as a λ value:

from terracodec import terracodec_v1_fp_s2l2a

model = terracodec_v1_fp_s2l2a(
    pretrained=True,
    compression=10
)

Temporal codec — pass compression as a λ value:

from terracodec import terracodec_v1_tt_s2l2a

model = terracodec_v1_tt_s2l2a(
    pretrained=True,
    compression=20
)

FlexTEC — one model for many compression levels, quality is specified at inference time (see below):

from terracodec import flextec_v1_s2l2a

model = flextec_v1_s2l2a(
    pretrained=True,
)

Alternative: TerraTorch Integration

TerraCodec models are also available through the TerraTorch model registry.

Install TerraTorch via pip. To ensure compatibility, we recommend installing TerraTorch from the main branch until v1.3 is released:

pip install terracodec "terratorch @ git+https://github.com/terrastackai/terratorch@main"

Models can then be instantiated directly via the registry:

from terratorch import FULL_MODEL_REGISTRY

model = FULL_MODEL_REGISTRY.build(
    "terracodec_v1_fp_s2l2a",
    pretrained=True,
    compression=10
)

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Input Format

Tensor shapes

Codec type	Shape	Example
Image codecs	[B, C, H, W]	[1, 12, 256, 256]
Temporal codecs	[B, T, C, H, W]	[1, 4, 12, 256, 256]

• 12 spectral bands (Sentinel-2 L2A) or 13 bands (L1C)
• Spatial size: 256×256 recommended. TEC-FP accepts arbitrary sizes; all other models expect 256×256.
• Temporal models: Models are pretrained on four seasonal frames but can process an arbitrary number of input timesteps at inference time. Using more frames increases the computational cost and therefore the required inference time.

Normalization

All models are pretrained on Training used SSL4EO-S12 v1.1.

Inputs should be standardized per spectral band using SSL4EO-S12 v1.1 L2A statistics:

mean = torch.tensor([793.243, 924.863, 1184.553, 1340.936, 1671.402, 2240.082, 2468.412, 2563.243, 2627.704, 2711.071, 2416.714, 1849.625])
std = torch.tensor([1160.144, 1201.092, 1219.943, 1397.225, 1400.035, 1373.136, 1429.170, 1485.025, 1447.836, 1652.703, 1471.002, 1365.307])

For S2L1C, similarly use:

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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Inference

Forward pass (fast, no bitstream)

# Image codec
reconstruction = model(inputs)

# Temporal codec
reconstruction, _ = model(sequence)

Compress / decompress (true bitstream)

# Image or temporal codec
compressed = model.compress(inputs)
reconstruction = model.decompress(**compressed)

print(compressed["bits"])   # total bits

FlexTEC

# Quality 1–16: lower = higher compression
compressed = model.compress(sequence, quality=8)
reconstruction = model.decompress(compressed)

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Examples & Notebooks

The notebooks/ directory contains end-to-end examples:

Notebook	Description
terracodec_fp_usage.ipynb	TEC-FP image codec walkthrough
terracodec_elic_usage.ipynb	TEC-ELIC image codec walkthrough
terracodec_tt_usage.ipynb	TEC-TT temporal codec walkthrough

Example Sentinel-2 images are in examples/.

For running these examples, clone and set up the repo with:

git clone https://github.com/IBM/TerraCodec.git
cd TerraCodec
python -m venv venv
source venv/bin/activate
pip install -e . # Install terracodec dependencies
pip install -r requirements.txt  # Install packages for data loading

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FAQ

Reconstruction quality is poor

1. Check preprocessing — verify band order, reflectance scaling, and per-band normalization.
2. GPU nondeterminism — entropy coding is sensitive to nondeterministic GPU operations. Enable deterministic mode:

os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":16:8")
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(True)

If CPU and GPU results differ, nondeterminism is likely the cause.

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Citation

@article{terracodec2025,
  title   = {TerraCodec: Compressing Optical Earth Observation Data},
  author  = {Costa-Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt and Schindler, Konrad},
  journal = {arXiv preprint arXiv:2510.12670},
  year    = {2025},
  doi     = {10.48550/arXiv.2510.12670}
}

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License

Apache 2.0 — see LICENSE.

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Acknowledgments

This research is carried out as part of the Embed2Scale project and is co-funded by the EU Horizon Europe program under Grant Agreement No. 101131841. Additional funding for this project has been provided by the Swiss State Secretariat for Education, Research and Innovation (SERI) and UK Research and Innovation (UKRI).