Overview of Esa Earth Observation Cubesats Missions

Pastena, Massimiliano; Melega, Nicola

doi:10.1117/12.2597557

Cited by 2 publications

(1 citation statement)

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“…Φsat-2 mission is part of an European Space Agency (ESA) initiative to promote the development and implementation of EO innovative technologies combining the fast development and reduced cost of new space with innovative EO concepts. The first Φsat related activities were kicked off in 2018 [1], with the aim of enhancing the already ongoing FSSCAT [2] mission with Artificial Intelligence (AI). Φsat-1 experiment successfully flown in 2020 [3] demonstrating the benefits of edge computing through AI and paving the way for its successor: Φsat-2.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Φsat-2 on board image processing chain

Melega,

Longepe,

Marchese

et al. 2023

Sensors, Systems, and Next-Generation Satellites XXVII

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The Φsat-2 mission from the European Space Agency (ESA) is part of Φsat mission lineup aimed to address innovative mission concepts making use of advanced onboard processing including Artificial Intelligence. Φsat-2 is based on a 6U CubeSat with a medium-high resolution VIS/NIR multispectral payload (eight bands plus NIR) combined with a hardware accelerated unit capable of running several AI applications throughout the mission lifetime. As images are acquired, and after the application of dTDI processing, the raw data is transferred through SpaceWire to a payload pre-processor where level L1B will be produced. At this stage radiometric and geometric processing are carried out in conjunction with georeferencing. Once the data is pre-processed, it is fed to the AI processor through the primary computer and made available to the onboard applications; orchestration is done via a dedicated version of the NanoSat MO Framework. The following applications are currently baselined and additional two will be selected via dedicated AI Challenge by Q3 2023: SAT2MAP for autonomous detection of streets during emergency scenarios; Cloud Detection application and service for data reduction; the Autonomous Vessel Awareness to detect and classify vessel types and the deep compression application (CAE) that has the goal of reducing the amount of acquired data to improve the mission effectiveness.

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Section: Introductionmentioning

confidence: 99%