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.

 

EU Flag  NEANIAS is a Research and Innovation Action funded by European Union under Horizon 2020 research and innovation programme via grant agreement No.863448.