---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- Buildings and Structures
- Desert
- Forest Area
- Hill or Mountain
- Ice Glacier
- Sea or Ocean
- Street View
- Image-Net
size_categories:
- 10K<n<100K
---

# **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).

---

*Curated & Maintained by [@prithivMLmods](https://huggingface.co/prithivMLmods).*