Efficient Subnets for Scalable Onboard AI in Space

There are currently a number of issues with deploying AI onboard satellites, some of the largest being the efficiency and adaptability of the Neural Network. Compounding this, efficient onboard AI solutions are not scaleable for use in satellite constellations. Adapting novel techniques from prior state of the art research, namely “Once-for-All: Train One Network and Specialize it for Efficient Deployment”, this project aims to develop state of the art methods and approaches for implementing efficient onboard space AI in the context of Earth Observation nanosatellite constellations. This directly relates to SmartSat’s vision to be recognised as a leading contributor in transforming Australia’s space innovation, as well as SmartSat’s mission to create game-changing technologies.

This research aligns with the SmartSat research Program 2 as the methods developed have broader applications within machine learning applications in space. This could enable effective AI solutions for each of the topics mentioned. Additionally, the research also aligns with research program 3 as we are focusing on the specific application context of using AI systems to analyse satellite generated data in real time.

This research will focus on the onboard AI problems within the context of Earth Observation, making its potential applications as diverse as Earth Observation applications. Notable applications include, agricultural monitoring, disaster warning systems, and environmental system monitoring and management.


Project Leader:
Professor Clinton Fookes, Queensland University of Technology

PhD Student:
Jordan Shipard, Queensland University of Technology