Update README.md

README.md

@@ -2,6 +2,13 @@
 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
@@ -24,10 +31,10 @@ Compared to classical codecs such as JPEG2000 or WebP, TerraCodec achieves **3
 
 | Model | Available Checkpoints | Description |
 |---|---|---|
-| `terracodec_v1_fp_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` | λ = 0.5, 2, 10, 40, 200 | Enhanced entropy model with spatial and channel context for improved rate–distortion performance. |
-| `terracodec_v1_tt_s2l2a` | λ = 0.4, 1, 5, 20, 100, 200, 700 | Temporal Transformer codec modeling redundancy across seasonal image sequences. |
-| `flextec_v1_s2l2a` | **Single checkpoint** (quality = 1–16) | Flexible-rate temporal codec. One model supports multiple compression levels via token-based quality settings. |
+| [`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**
@@ -38,12 +45,12 @@ Higher λ / quality → **higher reconstruction quality**
 
 This repository contains the **TEC-ELIC** variants of TerraCodec.
 
-[![TEC-ELIC Architecture](assets/TEC_image_architecture.png)](assets/TEC_image_architecture.png)
+![assets/TEC_ELIC_architecture.png](https://raw.githubusercontent.com/IBM/TerraCodec/main/assets/TEC_image_architecture.png)
 
 TEC-ELIC is a convolutional encoder–decoder neural compression model based on the Efficient Learned Image Compression (ELIC) architecture. The model predicts the mean and scale of latent variables using spatial and channel context together with a hyperprior. 
 This richer entropy model improves rate–distortion performance compared to the factorized prior model, at the cost of higher computational complexity. TEC-ELIC is optimized for 12-band Sentinel-2 imagery.
 
-See the paper for additional architectural and training details.
+See the [paper](https://arxiv.org/abs/2510.12670) for additional architectural and training details.
 
 ---
 
@@ -107,8 +114,8 @@ GitHub repository: https://github.com/IBM/TerraCodec
 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},
+  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}
 }