Home Posters P2.43s Semi-Supervised Learning for Automatic Improvement of Onboard Object Detection Models on Small Satellites P2.43s Semi-Supervised Learning for Automatic Improvement of Onboard Object Detection Models on Small Satellites Advanced Satellite Systems Sensors & Intelligence Lucas Tsutsui da Silva, The University of New South Wales This project aims to advance the state-of-the-art of onboard machine learning for small satellites and produce a new class of intelligent satellite systems capable of automatically refining onboard object detection models on satellites in orbit. Download Related Posters Advanced Satellite Systems Sensors & Intelligence Sai Vallapureddy, RMIT University P2.16s A Machine Learning Based Solution for Space Situational Awareness and Space Sustainability Advanced Satellite Systems Sensors & Intelligence David Shorten, The University of Adelaide P2.11 Trusted AI Frameworks for Change and Anomaly Detection in Observed ISR This project seeks to automate the identification of higher order... Advanced Satellite Systems Sensors & Intelligence Stefan Peters, University of South Australia P2.38 Small satellite energy-efficient on-board AI processing of hyperspectral imagery for early fire-smoke detection This research aims to provide a solution for energy-efficient...