Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
metadata
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 |
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.
Usage
You can load the dataset using the datasets library:
from datasets import load_dataset
dataset = load_dataset("prithivMLmods/Multilabel-GeoSceneNet-16K")
To visualize an example:
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.
Curated & Maintained by @prithivMLmods.