Update README.md

README.md

@@ -1,5 +1,65 @@
 ---
+license: apache-2.0
 library_name: terratorch
+datasets:
+  - ibm-esa-geospatial/TerraMesh
+tags:
+  - Earth Observation
+  - TerraMind
+  - IBM
+  - ESA
 ---
 
-Model weights for the TerraMind 1.0 Tokenizer for LULC classes (9 classes from ESRI WorldCover). 
+# TerraMind 1.0 LULC Tokenizer
+
+TerraMind is the first multimodal any-to-any generative foundation model for Earth Observation jointly developed by IBM, ESA, and Forschungszentrum Jülich.
+The model is pre-trained using FSQ-VAE tokens as targets. This tokenizer encodes and decodes land-use land-cover (LULC) maps for the TerraMind model.
+
+![lulc_tokenizer.png](assets%2Flulc_tokenizer.png)
+
+The tokenizer uses FSQ with five dimensions and a codebook size of 4'375 tokens.
+The model was pre-trained for 20 epochs on nine million LULC images from the TerraMesh dataset which are sourced from [ESRI](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02).
+The maps include nine classes and a 10th no-data class: No data, water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland.
+
+## Usage
+
+The tokenizer is fully integrated into the fine-tuning toolkit [TerraTorch](https://ibm.github.io/terratorch/).
+You can initialize the pre-trained tokenizer with:
+
+```python
+from terratorch.registry import FULL_MODEL_REGISTRY
+model = FULL_MODEL_REGISTRY.build('terramind_v1_tokenizer_lulc', pretrained=True)
+```
+
+Once the model is build, it can be used to encode image and decode tokens. 
+
+```python 
+# Encode image
+_, _, tokens = model.encode(lulc_tensor)
+# Decode tokens
+reconstruction = model.decode_tokens(tokens)
+# Encode & decode
+reconstruction = model(lulc_tensor)
+```
+
+This tokenizer is automatically loaded with TerraMind generation models like `terramind_v1_base_generate`, see [here](https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base#generations) for details.
+
+We provide example code for the tokenizer at https://github.com/IBM/terramind.
+
+
+## Feedback
+
+If you have feedback or any questions, please start a discussion in this HF repository or submitting an issue to [TerraMind](https://github.com/IBM/terramind) on GitHub.
+
+## Citation
+
+If you use TerraMind in your research, please cite our [TerraMind](https://arxiv.org/abs/2504.11171) pre-print.
+
+```text
+@article{jakubik2025terramind,
+  title={TerraMind: Large-Scale Generative Multimodality for Earth Observation},
+  author={Jakubik, Johannes and Yang, Felix and Blumenstiel, Benedikt and Scheurer, Erik and Sedona, Rocco and Maurogiovanni, Stefano and Bosmans, Jente and Dionelis, Nikolaos and Marsocci, Valerio and Kopp, Niklas and others},
+  journal={arXiv preprint arXiv:2504.11171},
+  year={2025}
+}
+```