Update README

README.md

@@ -3,6 +3,7 @@ license: cc-by-sa-4.0
 pretty_name: TerraMesh
 size_categories:
 - 10M<n<100M
+viewer: false
 ---
 
 # TerraMesh
@@ -10,7 +11,7 @@ size_categories:
 > **A planetary‑scale, multimodal analysis‑ready dataset for Earth‑Observation foundation models.**
 
 
-**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. Compared with earlier public corpora it offers **>20×** more pixel values and truly global, multi‑season coverage.
+**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.
 
 **Dataset release – End of June.**
 
@@ -48,7 +49,11 @@ Heat map of the sample count in a one-degree grid. |  Monthly distribution of al
 
 ## Performance evaluation
 
-![radar.png](assets%2Fradar.png)[radar.pdf](assets%2Fradar.pdf)
+![radar.png](assets%2Fradar.png)
+
+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.
 
 ---