Update README.md

README.md

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 - Hierarchical classification
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-`Messis` is an advanced crop classification model for the agricultural landscapes of Switzerland. It is built upon the geospatial foundation model [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M), which was originally pre-trained on U.S. satellite data. Messis has been trained using our ZueriCrop 2.0 dataset, a collection of Sentinel-2 imagery combined with ground-truth crop labels that covers agricultural regions in Switzerland. 
+`Messis` is a crop classification model for the agricultural landscapes of Switzerland. It is built upon the geospatial foundation model [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M), which was originally pre-trained on U.S. satellite data. Messis has been trained using our ZueriCrop 2.0 dataset, a collection of Sentinel-2 imagery combined with ground-truth crop labels that covers agricultural regions in Switzerland. 
 
 The Messis model leverages a three-tier hierarchical label structure, optimized for remote sensing tasks, to enhance its classification accuracy across different crop types. By adapting Prithvi to the specific challenges of Swiss agriculture—such as smaller field sizes and higher image resolutions by the Sentinel-2 satellites—Messis demonstrates the versatility of pretrained geospatial models in handling new downstream tasks.