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.

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Curated & Maintained by @prithivMLmods.