Add task category, library name, and update paper links

README.md

@@ -1,19 +1,23 @@
 ---
 license: cc-by-sa-4.0
-pretty_name: TerraMesh
 size_categories:
 - 1M<n<10M
+pretty_name: TerraMesh
 viewer: false
 tags:
 - Earth observation
 - Multimodal
 - Pre-training
+task_categories:
+- image-feature-extraction
+library_name: webdataset
 ---
 
 # TerraMesh
 
 > **A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models.**
 
+Paper: [TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data](https://huggingface.co/papers/2504.11172)
 
 **TerraMesh** merges data from **Sentinel‑1 SAR, Sentinel‑2 optical, Copernicus DEM, NDVI and land‑cover** sources into more than **9 million co‑registered patches** ready for large‑scale representation learning.
 
@@ -89,7 +93,7 @@ Heat map of the sample count in a one-degree grid. |  Monthly distribution of al
 TerraMesh was used to pre-train [TerraMind-B](https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base).
 On the six evaluated segmentation tasks from PANGAEA bench, TerraMind‑B reaches an average mIoU of 66.6%, the best overall score with an average rank of 2.33. This amounts to roughly a 3pp improvement over the next‑best open model (CROMA), underscoring the benefits of pre‑training on TerraMesh.
 Compared to an ablation model pre-trained only on SSL4EO-S12 locations TerraMind-B performs overall 1pp better with better global generalization on more remote tasks like CTM-SS.
-More details in our [paper](https://arxiv.org/abs/2504.11172).
+More details in our [paper](https://huggingface.co/papers/2504.11172).
 
 ---
 
@@ -260,7 +264,8 @@ If you use TerraMesh, please cite:
   title={Terramesh: A planetary mosaic of multimodal earth observation data},
   author={Blumenstiel, Benedikt and Fraccaro, Paolo and Marsocci, Valerio and Jakubik, Johannes and Maurogiovanni, Stefano and Czerkawski, Mikolaj and Sedona, Rocco and Cavallaro, Gabriele and Brunschwiler, Thomas and Bernabe-Moreno, Juan and others},
   journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
-  year={2025}
+  year={2025},
+  url={https://huggingface.co/papers/2504.11172}
 }
 ```
 
@@ -282,4 +287,4 @@ The LULC data is provided by [ESRI, Impact Observatory, and Microsoft](https://p
 
 The cloud masks used for augmentating the LULC maps and provided as metadata are produced using the [SEnSeIv2](https://github.com/aliFrancis/SEnSeIv2/tree/main?tab=readme-ov-file) model.
 
-The DEM data is produced using [Copernicus WorldDEM-30](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM) © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved
+The DEM data is produced using [Copernicus WorldDEM-30](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM) © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved