Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 3,149 Bytes
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---
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) |
|------------------------------|---------------------------|
|  | Buildings and Structures |
|  | 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).* |