Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+---
+Here’s a `README.md` you can use for the **Multilabel-GeoSceneNet-16K** dataset based on your screenshot and label information:
+
+---
+
+```markdown
+# Multilabel-GeoSceneNet-16K
+
+**Multilabel-GeoSceneNet-16K** is a geospatial image dataset for **multi-label scene classification**. Each image may belong to one or more geographic scene categories, making it suitable for multi-label learning tasks in remote sensing and geospatial analytics.
+
+## Dataset Summary
+
+- **Task**: Multi-label Image Classification
+- **Modalities**: Image
+- **Total Images**: 16,033
+- **Split**: Train (100%)
+- **Labels**: 7 categories (multi-label)
+- **License**: Apache-2.0
+- **Size**: ~227 MB
+
+## Labels
+
+Each image may be annotated with one or more of the following scene categories:
+
+| Label ID | Class Name              |
+|----------|--------------------------|
+| 0        | Buildings and Structures |
+| 1        | Desert                   |
+| 2        | Forest Area              |
+| 3        | Hill or Mountain         |
+| 4        | Ice Glacier              |
+| 5        | Sea or Ocean             |
+| 6        | Street View              |
+
+```py
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("prithivMLmods/Multilabel-GeoSceneNet-16K")
+
+# Extract unique labels
+labels = dataset["train"].features["label"].names
+
+# Create id2label mapping
+id2label = {str(i): label for i, label in enumerate(labels)}
+
+# Print the mapping
+print(id2label)
+```
+
+```
+{'0': 'Buildings and Structures', '1': 'Desert', '2': 'Forest Area', '3': 'Hill or Mountain', '4': 'Ice Glacier', '5': 'Sea or Ocean', '6': 'Street View'}
+```
+
+## Features
+
+| Column | Type   | Description                                 |
+|--------|--------|---------------------------------------------|
+| image  | Image  | Image input in JPEG format                  |
+| label  | List   | List of class labels for the given image    |
+
+## Example
+
+| Image                         | Label(s)                  |
+|------------------------------|---------------------------|
+| ![](sample1.png)             | Buildings and Structures  |
+| ![](sample2.png)             | Forest Area, Hill or Mountain |
+
+> Note: For best experience, browse the dataset directly on [Hugging Face](https://huggingface.co/datasets/prithivMLmods/Multilabel-GeoSceneNet-16K).
+
+## Usage
+
+You can load the dataset using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("prithivMLmods/Multilabel-GeoSceneNet-16K")
+```
+
+To visualize an example:
+
+```python
+import matplotlib.pyplot as plt
+
+example = dataset['train'][0]
+plt.imshow(example['image'])
+plt.title(", ".join(example['label']))
+plt.axis('off')
+plt.show()
+```
+
+## Applications
+
+- Geospatial scene understanding
+- Remote sensing analytics
+- Environmental monitoring
+- Land cover classification
+- AI-assisted mapping
+
+## License
+
+This dataset is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+
+---
+
+*Maintained by [@prithivMLmods](https://huggingface.co/prithivMLmods).*
+```