The-Structure detection on large map images with machine learning techniques consists of three services, namely SPACE-ML CAESAR, SPACE-ML Astro ML and SPACE-ML LSE.
SPACE-ML CAESAR
CAESAR (Compact And Extended Sources Automated Recognition) service provides a straightforward solution to segment astrophysical FITS maps, allowing for the extraction and characterization of both compact (e.g. stars, galaxies) and extended sources (e.g. galactic filaments, supernovae remnants).
SPACE-ML Astro ML
These tools are Deep Learning models included and accessed by the SPACE-ML CAESAR service and also made available through the C3.1 AI Science Gateway.
SPACE-ML LSE service
Latent Space Explorer (LSE) support analysis of image datasets via unsupervised machine learning methods. It allows to extract a compact representation from data by representation learning models (e.g. autoencoders). The information extracted can be then visualized using the projector. The latter allows visualizing the data in a 2D or 3D space in an interactive fashion. The system then allows performing clustering algorithms to detect potentially relevant ways to group images and to support the definition of novel classification schemes.