Advancing Planetary Science through Integrated Tools and Services at the Space Science Data Center (SSDC) of the Italian Space Agency (ASI).

 

Introduction 

The Italian Space Agency’s Space Science Data Center (SSDC) has made significant progress creating a unified, scalable, and accessible system for planetary science analysis. At the heart of this transformation is MATISSE, a user-friendly web-based platform that streamlines the integration, visualization, and analysis of planetary data from various missions. MATISSE stands for Multi-purpose Advanced Tool for Instruments of the Solar System Exploration [1]. With MATISSE and a new HPC-powered JupyterHub environment, SSDC facilitates interdisciplinary collaboration, automated complex analyses, and optimized data use for present and future planetary missions among scientists and engineers. 

1 MATISSE today 

Multi-mission reach. Datasets from Mars, Mercury, Ceres, Vesta, Venus, Didymos, and—newly—Earth’s Moon (LRO LOLA, NAC/WAC, Diviner; Chandrayaan-1 M³) are delivered through a uniform REST API and visualised in 2-D/3-D canvases. 

Surface-to-interior view. Geological maps [2] and MARSIS radargrams now sit alongside hyperspectral, thermal, and elevation layers, allowing true cross-disciplinary queries—from stratigraphy to crustal structure. 

Automation ready. Query results are streamed as FITS/GeoTIFF; identical endpoints feed interactive notebooks or scripted pipelines. 

2 New lunar & hyperspectral products 

Automated workflows ingest raw LRO and M³ telemetry to generate: 

High-resolution digital terrain models with derived slope / roughness rasters; 

Spectral index products from M³; 

Cubes representing thermal anomalies connect surface temperature variations with properties of the regolith. Comparing lunar, Martian, and Mercurian terrains side-by-side, these products promote comprehensive planetary science. 

3 Mission-oriented pipelines 

This same infrastructure, with its robust systems and automated workflows, supports pre-built analyses, integrating the entire process. An example, the landing-site-selection pipeline, combines: 

Engineering layers (slope, roughness, solar flux, Earth line-of-sight) derived on-the-fly from DEMs and mosaics; 

Science layers (geology, compositional indices, thermal inertia) fetched from MATISSE; 

Custom weighting to generate GIS-compatible suitability maps. The browser lets you adjust the workflow, which you can then export as GIS-compatible files, bridging the gap between engineers and scientists. 

4 JupyterHub + Docker + HPC 

The Europlanet GMAP JupyterHub [3], a containerized JupyterHub for processing planetary data, has been deployed by SSDC next to the archive. 

Reproducible stacks. Pre-built Docker images pack ISIS, the Ames Stereo Pipeline, and a curated GeoPython tool-set. 

Scalable execution. Prototyping on a laptop or scaling to SSDC’s clusters, the same Docker image runs with consistent dependencies and paths for massive processing. 

Collaborative notebooks on SSDC’s JupyterHub fuse real-time co-editing with GitHub/GitLab versioning, cloud-drive mounts and shared team folders. 

5 Impact & outlook 

By collapsing archiving, heavy computation, and collaborative interpretation into one modular platform, SSDC delivers: 

End-to-end acceleration of research process with streamlined transition from raw telemetry to publication-ready products in a unified setting. Reduced complexity for beginners through GUI and advanced capabilities for experienced users via full API. Customizable and automated workflows with forkable Docker images and scheduled jobs for efficiency. Real-time collaboration facilitated through shared notebooks for geographically dispersed research teams. Ensuring reproducibility in scientific endeavors through container tags and version-controlled code. 

Upcoming work will focus on persistent workspace snapshots, better integration with mission operations pipelines, and bulk release of analysis-ready data. Data-rich, multi-agency planetary exploration ventures can use the scalable SSDC model for cross-disciplinary innovation. 

Funding 

Supported by ASI-INAF agreement n. 2022-14-HH.0. 

References 

[1] A. Zinzi, M.T. Capria, E. Palomba, P. Giommi, L.A. Antonelli, (2016) Astronomy and Computing, Volume 15, 2016,Pages 16-28, ISSN 2213-1337, https://doi.org/10.1016/j.ascom.2016.02.006. [2] V. Camplone, A. Zinzi, M. Massironi, A.P. Rossi, F. Zucca, (2024) Astronomy and Computing, Volume 48, 2024, 100852, ISSN 2213-1337, https://doi.org/10.1016/j.ascom.2024.100852. [3] A. Zinzi et al., (2021), 5th Planetary Data and PSIDA 2021 (LPI Contrib. No. 2549), [3] Giacomo Nodjoumi, C H Brandt, J E Suárez-Valencia, et al. Collaborative and Reproducible planetary science through the Europlanet GMAP JupyterHub processing environment. ESS Open Archive . March 06, 2025. DOI: 10.22541/essoar.174129212.22376025/v1