SpaceNet: A Comprehensive Astronomical Dataset

Description: SpaceNet: A Comprehensive Astronomical Dataset, obtained via a novel double-stage augmentation framework called FLARE is a hierarchically structured and high-quality astronomical image dataset. It is meticulously designed for both fine-grained and macro classification tasks. Comprising approximately 12,900 samples, SpaceNet incorporates lower (LR) to higher resolution (HR) conversion with standard augmentations and a diffusion approach for synthetic sample generation. This comprehensive dataset enables superior generalization on various recognition tasks, including classification. Download Dataset Key Features High-Resolution Images: The dataset includes high-quality images that facilitate accurate analysis and classification. Hierarchical Structure: The dataset is hierarchically organized to support both macro and fine-grained classification tasks. Advanced Augmentation Techniques: Utilizes FLARE framework for double-stage augmentation, enhancing the dataset’s diversity and robustness. Synthetic Sample Generation: Employs a diffusion approach to create synthetic samples, boosting the dataset’s size and variability. Usage SpaceNet is ideal for: Training and Evaluation: Developing and testing machine learning models for fine-grained and macro astronomical classification tasks. Research: Exploring hierarchical classification approaches within the astronomy domain. Model Development: Creating robust models capable of generalizing across both in-domain and out-of-domain datasets. Educational Purposes: Providing a rich dataset for educational projects in astronomy and machine learning. This dataset is sourced from Kaggle.