Many use cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points – a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

The Ka-band frequency (35 GHz) is becoming increasingly attractive for spaceborne remote sensing applications. Unlike other radar bands, the Ka-band enables the development of compact high resolution interferometric instruments and embarking the payloads on a single platform. This creates unique new opportunities in Earth observation science and civil security applications, for instance in monitoring the changing cryosphere, generating high resolution maps of ocean currents and ship and vehicle detection.

The goal of the project was to develop a service as a product to simplify fitting DNN (Deep Learning Neural Networks) in on board HW to make better use of AI on space missions The role of the service is to support data processing engineers in reducing HW resources requirement of powerful DNN for image analysis to be executed onboard. 

Space missions benefit greatly by the capability of the on-board GNC system to adapt rapidly to unknown environment. Autonomous vision-based navigation is a particular technology under implementation in several ESA missions. One of the most interesting applications in that field is the proximity operations around a small asteroid, like those in HERA.
The goal of this activity was to develop a navigation algorithm with the capability to fly over an unknown terrain and achieve better navigation performances than current vision-based techniques based on unknown feature tracking.

•On-board disturbance sources such as solar array drive mecha

Quantum algorithms process information stored in qubits, the basic memory unit of quantum processors, and quantum operations (called gates) are the building blocks of quantum algorithms. In order to be run on quantum computing hardware, quantum algorithms must be compiled into a set of elementary machine instructions (i.e., quantum gates).

The E.Inspector mission objectives are to:* Image an ESA owned space debris in its current status * Use obtained images for the verification and validation of the e.deorbit or space tug GNC sensorsFollowing a successful ESA internal CDF study, the mission is now proposed to go to phase A to be studied by industry.Several drivers make this mission design challenging:* A possible long and large (delta-V) transfer to the target* Compliance to Space Debris Mitigation guidelines from possibly high altitude* A close RDV while maintaining keep-out zones and ensuring passively safe trajectories* Tr