SpaceNet Comprehensive Astronomical Dataset

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SpaceNet Comprehensive Astronomical Dataset

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SpaceNet Comprehensive Astronomical

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SpaceNet Comprehensive Astronomical

Description

Discover the SpaceNet Comprehensive Astronomical Dataset, featuring 12,900 high-resolution images of planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.

Description:

SpaceNet is a hierarchically structured and high-quality astronomical image dataset, created using a novel double-stage augmentation process. This dataset, comprising approximately 12,900 images, is designed for both fine-grained and macro classification tasks. SpaceNet incorporates a range of resolutions from lower (LR) to higher resolution (HR) images, using standard augmentations and a diffusion approach for generating synthetic samples. This allows for superior generalization across various recognition tasks such as classification. The dataset also includes diverse celestial objects, making it a valuable resource for both academic research and practical applications in astronomy and astrophysics.

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Dataset Structure:

Fine-Grained Classes: The dataset includes 8 distinct classes: planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.

Dataset Composition:

Total Samples: Approximately 12,900 images
Fine-Grained Class Distribution:
- Asteroid: 283 images
- Black Hole: 656 images
- Comet: 416 images
- Constellation: 1,552 images
- Galaxy: 3,984 images
- Nebula: 1,192 images
- Planet: 1,472 images
- Star: 3,269 images

Usage: SpaceNet is ideal for:

Training and evaluating machine learning models on fine-grained and macro astronomical classification tasks.
Conducting research on hierarchical classification methods within the astronomy field.
Developing robust models that demonstrate excellent generalization across both in-domain and out-of-domain datasets.