Add `library_name` and sample usage, improve top-level links

README.md

@@ -13,13 +13,16 @@ tags:
 - pretrain
 - self-supervised-learning
 - sentinel
+library_name: datasets
 ---
 
 # Dataset Card for Copernicus-Pretrain
 
+[Paper](https://arxiv.org/abs/2503.11849) | [Repository](https://github.com/zhu-xlab/Copernicus-FM)
+
 <!-- Provide a quick summary of the dataset. -->
 
-[Copernicus-Pretrain](https://github.com/zhu-xlab/Copernicus-FM/tree/main) is a large-scale EO pretraining dataset with 18.7M aligned images covering all major Sentinel missions (S1,2,3,5P).
+Copernicus-Pretrain is a large-scale EO pretraining dataset with 18.7M aligned images covering all major Sentinel missions (S1,2,3,5P).
 
 *Officially named **Copernicus-Pretrain**, also referred to as SSL4EO-S ("S" means Sentinel), as an extension of [SSL4EO-S12](https://github.com/zhu-xlab/SSL4EO-S12) to the whole Sentinel series.*
 
@@ -42,13 +45,24 @@ The images are organized into ~310K regional grids (0.25°x0.25°, consistent wi
 | Copernicus DEM  | elevation | 30 m | 960×960   | 297,665     | 297,665   | 1           | 297,665       |
 | **Copernicus-Pretrain** |  |  |  | **312,567** | **3,879,597** |  | **18,713,054** |
 
+## Sample Usage
+
+You can load the dataset using the Hugging Face `datasets` library. This dataset is very large and may require specific handling such as streaming or selecting specific configurations if available.
 
-## Related Sources
+```python
+from datasets import load_dataset
 
-<!-- Provide the basic links for the dataset. -->
+# Load the dataset. For large datasets, consider streaming or specific data_files if available.
+# This dataset offers raw GeoTiff and streaming WebDataset formats.
+dataset = load_dataset("wangyi111/Copernicus-Pretrain")
 
-- **Repository:** https://github.com/zhu-xlab/Copernicus-FM
-- **Paper:** https://arxiv.org/abs/2503.11849
+# Print the dataset structure (e.g., available splits)
+print(dataset)
+
+# Example of accessing a sample from a split (uncomment and adjust if applicable)
+# For example, if 'train' split exists:
+# print(dataset["train"][0])
+```
 
 ## License