Expertise
We combine engineering precision and scientific depth to turn satellite data into reliable products, indicators, and decisions. Our expertise spans five connected areas, from instrument calibration to AI-driven analytics and academic collaboration.
Extracting value from massive satellite datasets.
We design scalable, automated pipelines that turn large volumes of satellite data into operational products. Our work combines advanced analytics, spatio temporal modeling, and forecasting — applying AI/ML where it measurably improves detection, classification, or product generation for end users and missions.
What we work on:
- Automated, reproducible processing pipelines.
- Spatio-temporal modeling and predictive analytics.
- Detection and classification algorithms.
- Forecasting for environmental and operational applications.
- Scalable infrastructure for large EO datasets
Making sure every measurement counts
We help space instruments and processing chains meet their scientific and operational goals: from low-level signal processing to high-level Earth observation product quality assurance. Our focus areas include precision calibration in microwave and multi/hyperspectral domains, where small errors translate into significant scientific impact.
What we work on:
- Radiative transfer modeling and end-to-end simulations
- Processing algorithm design and Level 0 → Level 1 calibration
- Quality assurance for low- and high-level EO products
- Performance, complexity, and schedule trade-off analysis
- Technical coordination across instrument and mission teams
EO at the service of the planet and the people who decide for it.
We develop and apply solutions to monitor ecosystems and planetary health, with R&D spanning aquatic pollution, climate-relevant variables, and rapidly changing polar and ocean systems. Our work is designed to transfer into real decisions, in close collaboration with public administrations and international bodies.
What we work on:
- Aquatic pollution monitoring, including marine litter
- Essential Climate Variables (ECVs) from EO data
- Climate change indicators in polar regions and oceans
- Early-warning and prediction systems (e.g. heatwaves)
- Knowledge transfer to policy and decision-making
AI where it makes a real difference.
We develop machine learning pipelines and apply deep learning to EO tasks where it clearly outperforms traditional approaches, particularly in environmental detection. In the radiofrequency domain, AI accelerates interference detection and mitigation and powers predictive models, always integrated into reproducible, operational processing flows.
What we work on:
- ML pipeline design and deployment
- Deep learning for environmental detection and classification
- AI-based RFI detection and mitigation
- Predictive models for instrument and signal behavior
- Reproducible, operations-ready AI workflows
Closing the loop between research and industry.
We keep an active link with universities and research institutions, because cutting-edge EO work depends on it. Our team teaches, mentors, publishes, and reviews, sustaining a two-way flow of knowledge between academia and the space and industrial sectors.
What we work on:
- Teaching at undergraduate and master's level
- Master's thesis supervision and PhD training
- Peer-reviewed scientific publications, including open access
- Journal peer review
- Knowledge transfer initiatives across research and industry
