Instructions to use embed2scale/TerraCodec-1.0-FlexTEC-S2L2A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TerraTorch
How to use embed2scale/TerraCodec-1.0-FlexTEC-S2L2A with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("embed2scale/TerraCodec-1.0-FlexTEC-S2L2A") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: apache-2.0
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paper: https://arxiv.org/abs/2510.12670
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homepage: https://github.com/IBM/TerraCodec
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---
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# TerraCodec
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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.
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 | Flexible-rate temporal codec. One model supports multiple compression levels via token-based quality settings. |
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Lower λ / quality → **higher compression**
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Higher λ / quality → **higher reconstruction quality**
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This repository contains the **FlexTEC** variant of TerraCodec.
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FlexTEC builds on the TerraCodec Temporal Transformer (TEC-TT) architecture and introduces **flexible-rate compression within a single model**.
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Instead of training separate checkpoints for different rate–distortion settings, FlexTEC supports multiple compression levels through Latent Repacking and missing token prediction. During training, tokens are randomly masked to simulate different compression budgets. At inference time, the number of transmitted tokens controls the compression level: fewer tokens yield higher compression, while more tokens improve reconstruction quality.
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See the paper for additional architectural and training details.
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---
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If you use TerraCodec in your research, please cite:
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```
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@article{terracodec2025,
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title = {TerraCodec:
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author = {Costa Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt},
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journal = {arXiv preprint arXiv:2510.12670},
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year = {2025}
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}
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license: apache-2.0
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paper: https://arxiv.org/abs/2510.12670
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homepage: https://github.com/IBM/TerraCodec
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datasets:
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- embed2scale/SSL4EO-S12-v1.1
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library_name: terratorch
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tags:
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- Compression
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- Neutral Codecs
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- Earth Observation
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---
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# TerraCodec
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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.
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---
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| Model | Available Checkpoints | Description |
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| [`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. |
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| [`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. |
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| [`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. |
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| [`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. |
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Lower λ / quality → **higher compression**
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Higher λ / quality → **higher reconstruction quality**
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This repository contains the **FlexTEC** variant of TerraCodec.
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FlexTEC builds on the TerraCodec Temporal Transformer (TEC-TT) architecture and introduces **flexible-rate compression within a single model**.
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Instead of training separate checkpoints for different rate–distortion settings, FlexTEC supports multiple compression levels through Latent Repacking and missing token prediction. During training, tokens are randomly masked to simulate different compression budgets. At inference time, the number of transmitted tokens controls the compression level: fewer tokens yield higher compression, while more tokens improve reconstruction quality.
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See the [paper](https://arxiv.org/abs/2510.12670) for additional architectural and training details.
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---
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If you use TerraCodec in your research, please cite:
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```
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@article{terracodec2025,
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title = {TerraCodec: Compressing Optical Earth Observation Data},
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author = {Costa Watanabe, Julen and Wittmann, Isabelle and Blumenstiel, Benedikt and Schindler, Konrad},
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journal = {arXiv preprint arXiv:2510.12670},
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year = {2025}
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}
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