---
license: apache-2.0
paper: https://arxiv.org/abs/2510.12670
homepage: https://github.com/IBM/TerraCodec
datasets:
- embed2scale/SSL4EO-S12-v1.1
library_name: terratorch
tags:
- Compression
- Neutral Codecs
- Earth Observation
---

# TerraCodec

**Neural Compression for Earth Observation**

[![arXiv](https://img.shields.io/badge/arXiv-2510.12670-b31b1b)](https://arxiv.org/abs/2510.12670)
[![GitHub](https://img.shields.io/badge/GitHub-IBM%2FTerraCodec-black?logo=github)](https://github.com/IBM/TerraCodec)
[![PyPI](https://img.shields.io/badge/PyPI-terracodec-blue?logo=pypi)](https://pypi.org/project/terracodec/)

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.

Compared to classical codecs such as JPEG2000 or WebP, TerraCodec achieves **3–10× higher compression at comparable reconstruction quality** on multispectral satellite imagery. Temporal models further improve compression by exploiting redundancy across seasonal image sequences of satellite imagery.

![Reconstructions](https://raw.githubusercontent.com/IBM/TerraCodec/main/assets/reconstructions.jpg)

---

# Model Family

| Model | Available Checkpoints | Description |
|---|---|---|
| [`terracodec_v1_fp_s2l2a`](https://huggingface.co/embed2scale/TerraCodec-1.0-ELIC-S2L2A) | λ = 0.5, 2, 10, 40, 200 | Factorized-prior image codec. Smallest model and strong baseline for multispectral image compression. |
| [`terracodec_v1_elic_s2l2a`](https://huggingface.co/embed2scale/TerraCodec-1.0-FP-S2L2A) | λ = 0.5, 2, 10, 40, 200 | Enhanced entropy model with spatial and channel context for improved rate–distortion performance. |
| [`terracodec_v1_tt_s2l2a`](https://huggingface.co/embed2scale/TerraCodec-1.0-TT-S2L2A) | λ = 0.4, 1, 5, 20, 100, 200, 700 | Temporal Transformer codec modeling redundancy across seasonal image sequences. |
| [`flextec_v1_s2l2a`](https://huggingface.co/embed2scale/TerraCodec-1.0-FlexTEC-S2L2A) | **Single checkpoint** (quality = 1–16) | Flexible-rate temporal codec. One model supports multiple compression levels via token-based quality settings. |

Lower λ / quality → **higher compression**  
Higher λ / quality → **higher reconstruction quality**

---

# Model Architecture

This repository contains the **TEC-FP (Factorized Prior)** variants of TerraCodec.

![assets/TEC_FP_architecture.png](https://raw.githubusercontent.com/IBM/TerraCodec/main/assets/TEC_FP_architecture.png)

TEC-FP is a convolutional encoder–decoder neural compression model with a fully factorized entropy model for the latent representation. Each quantized latent variable is modeled independently without spatial or channel context.
This design enables efficient parallel entropy coding. TEC-FP is the smallest and fastest image codec in the TerraCodec family and is optimized for 12-band Sentinel-2 imagery.

See the [paper](https://arxiv.org/abs/2510.12670) for additional architectural and training details.

---

# Input Format

| Codec type      | Expected shape      | Example                |
|-----------------|---------------------|-------------------------|
| Image codecs    | `[B, C, H, W]`      | `[1, 12, 256, 256]`     |
| Temporal codecs | `[B, T, C, H, W]`   | `[1, 4, 12, 256, 256]`  |

- Inputs use **12 Sentinel‑2 L2A spectral bands**.  
- Recommended spatial size: **256×256**. TEC‑FP supports arbitrary spatial sizes; other models expect 256×256.  
- Temporal codecs were pretrained on four seasonal frames, but can process any number of timesteps during inference (higher T increases compute).  

---

# Normalization

Models were trained on **[SSL4EO-S12 v1.1](https://huggingface.co/datasets/embed2scale/SSL4EO-S12-v1.1)**.

Inputs should be standardized per spectral band using dataset statistics. For S2L2A:

```python
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])
```

# Usage

Install TerraCodec:

```
pip install terracodec
```

Load pretrained models:
```python
from terracodec import terracodec_v1_fp_s2l2a

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

# 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: 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}
}
```