NEANIAS Space Research Community

The NEANIAS Space Research Community engages Open Science practices through the European Open Science Cloud (EOSC), targeting a wide variety of scientific and professional communities related toAstrophysics and Planetary Science engaging alsocomputer scientists and software engineers interested in computer vision and machine learning.NEANIAS Space Services data and products may also have ahigh impact in planetary mining and robotics, space weather and mobile telecommunications.

The NEANIAS Space Research services are aimed at supporting management and analysis of large data volumes in astrophysics and planetary sciences through visualization (SPACE-VIS services), efficiently generating large multidimensional maps and mosaics (SPACE-MOS services), and, finally, supporting mechanisms for automatic detection of structures within maps through machine learning (SPACE-ML services).

The SPACE-VIS services provide an integrated operational solution for astrophysics and planetary data management aided by advanced visualization mechanisms, including visual analytics and virtual reality, and it is underpinned by FAIR principles.

• The ViaLactea service accesses astrophysical surveys to aid understanding of the star formation process of the Milky Way. ViaLactea Visual Analytics (VLVA) combine different types of visualization to perform analysis by exploring correlations managed in the ViaLactea Knowledge Base (VLKB). VLKB includes 2D and 3D (velocity cubes) surveys, numerical model outputs, point-like and diffuse object catalogues and allows for retrieval of all available datasets as well as cut-outs on the positional and/or velocity axis.
• The Astra Data Navigator (ADN) is a virtual reality environment for visualizing large stellar catalogues. The first prototype has been customised to access cloud services for interactive data exploration and navigation with the ability of exploring advanced virtual reality mechanisms providing full immersion.
• Finally, the ADAM-Space Service (Advanced Geospatial Data Management platform) accesses a large variety of environmental data and is customised in NEANIAS to access planetary data.

The SPACE-MOS services provide tools for making high quality images from raw data (map making) and for assembling such images into custom mosaics (mosaicing).

• The AstroMapMerging service exploits Montage (http://montage.ipac.caltech.edu/) and is integrated with the ViaLactea servicefor merging adjacent datasets.

• The ISIS3 and ASP under ADAM-DPS service allows integration with data processing pipelines in ADAM which offers tools for planetary data analysis and for producing cartographic products, such as Digital Elevation Models (DEMs) and 3D models from stereo imagery.

The SPACE-ML services provide advanced solutions for pattern and structure detection in astronomical surveys as well as in planetary surface composition, topography and morphometry. The service integrates cutting-edge machine learning algorithms to perform automatic classification of compact and extended sky structures or planetary surfaces.

• CAESAR service allows to extract and parametrize compact and extended sources from astronomical radio interferometric maps. The processing pipeline consists of a series of distinct stages that can be run on multiple cores and processors.

• AstroML service has been developed to integrate a deep learning mechanism to significantly improve source identification, classification, and characterization of sources in large-scale radio surveys.

• The Latent Space Explorer service performs unsupervised representation learning of astronomical images using deep learning techniques (e.g., autoencoders) and interactive visualization of the representations with the chance to apply clustering methods in order to help the domain expert to understand the structure of the representation space.

Please visit also the NEANIAS Space thematic portalfor more info on the Space Services and the research community.

NEANIAS web portal:https://www.neanias.eu/

Please consider acknowledging the NEANIAS project if you use the results of this service in any paper or communication:NEANIAS is funded by European Union under Horizon 2020 research and innovation programme via grant agreement No. 863448.