SpaceNet Comprehensive Astronomical Dataset

Description:

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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.

This dataset is sourced from Kaggle.