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Area Engineering

Reliability Evaluation of Convolutional Neural Networks for Multispectral Images Segmentation on Earth Observation Microsatellites

azienda Thesis in external company    


keywords ARGOTEC, ARTIFICIAL INTELLIGENCE, EDGE COMPUTING, EDGE-AI, IRIDE, LOW EARTH ORBIT, MICROSATELLITE

Reference persons ANNACHIARA RUOSPO, EDGAR ERNESTO SANCHEZ SANCHEZ

External reference persons Lorenzo Sarti; Niccolò Battezzati

Research Groups DAUIN - GR-05 - ELECTRONIC CAD and RELIABILITY GROUP - CAD

Thesis type SOFTWARE SPERIMENTALE

Description In recent years, the Microsatellites market has grown rapidly thanks to their compact form factor, which allows for the reduction of launch and development costs typical of the space sector. In this context, Argotec, a leading company in the development of Microsats for both Deep Space and Low Earth Orbit (LEO) missions, participates in the Italian IRIDE program for Earth Observation with a constellation of satellites equipped with high resolution multispectral payloads. Argotec's technological platform, HAWK, will be able to count on a Payload Data Processor (PDP) for the implementation of AI algorithms on-the-edge.
Argotec proposes a thesis project for the development of a tool/framework for evaluating the reliability of convolutional neural networks for segmentation tasks on multispectral images on board these Microsats. Therefore, starting from the identification of relevant use cases in the context of the IRIDE program, a method will be defined for evaluating the reliability of the models selected for these use cases. The framework development phase and validation will follow directly on the Payload Data Processor selected for the constellation. Given the growing number of increasingly aware and autonomous satellite platforms, this thesis will contribute to the possibility of accurately evaluating the reliability of the models used on board, playing a key role in the success of the mission and the duration of the satellite in orbit.

Required skills • Theory of Machine Learning and Deep Learning
• Convolutional Neural Networks
• Python and C
• Linux based Operating Systems
• Notions on Reliability of AI Systems


Deadline 11/12/2024      PROPONI LA TUA CANDIDATURA