prithivMLmods/Multilabel-GeoSceneNet-16K

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:

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

Example

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](https://huggingface.co/prithivMLmods).