Research Programs

SpaceCraft Autonomy and Onboard AI for Next Generation Space Systems (Part of the SpaceCraft Autonomy Research Lab: SCARLET-⍺)

Trusted Autonomous Satellite Operations

Spacecraft autonomy has been recognised as a key enabler of the next-generation space systems that aim at increasing responsiveness and continuity of space-based observations, covering large areas with higher resolutions, minimizing communication and data access latencies, and reducing costs of both the space and ground segments.

Spacecraft autonomy encompasses onboard autonomous decision-making capabilities that enable the space segment to continue mission operations and to survive critical situations without relying on ground segment intervention. It relates to all aspects of spacecraft operations, including continuous mission planning and execution on board, real-time spacecraft control outside ground contact, maximisation of mission objectives in relation to the available onboard resources and capabilities of other spacecraft, and system robustness in presence of on-board failures and external uncertainties.

This project aims at addressing the above requirements by developing novel concepts, methods and technologies to provide new AI-based spacecraft autonomy capabilities for the next-generation space systems, such as dynamically networked formations of heterogeneous satellites. It focuses on high impact areas of spacecraft autonomy and onboard AI as identified and prioritised with the industry and defence partners, including:

  • WP1: Onboard processing and actionable intelligence
  • WP2: Small spacecraft and constellation resilience
  • WP3: Dynamic optimisation of constellation resources
  • WP4: Real-time tasking and resource allocation

The output of this project is a set of autonomous algorithms, demonstrating their capability through software simulations with use-cases provided by the industry partners.

This project will leverage and contribute to the IPC Capability Demonstrator with an aim to be demonstrated on DST RMS STaR Shot as a pathway to commercialisation of the developed technology solutions for industry partners.


Project Leader:
Professor Ryszard Kowalczyk, University of South Australia


An empirical and dynamic tool for prediction of forest fire spread using RS and ML techniques

EO Analytics

Australia has a total of 134 million hectares of forest, which is equivalent to 17% of Australia’s land area. Of this total forest area, determined as at 2016, 132 million hectares (98%) are ‘Native forests’, 1.95 million hectares are ‘Commercial plantations’, and 0.47 million hectares are ‘other forest’. Australia has about 3% of the world’s forest area and globally is the country with the seventh-largest forest area. The Australian ecosystem is shaped by fires for over 70 000 years and each ecosystem has its fire regimes. Additionally, fuel management and predicting flammable areas are the key to managing wildfires. These factors play a vital role in resource allocation, mitigation and recovery efforts. Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Therefore, it is important to know and understand the behaviour of fire ignition and spread so that fire management agencies can prevent and mitigate wildfires. This project aims to develop a tool to predict Forest Fire Spread using Machine Learning approach and Weather Research and Forecasting with fire spread model (WRF-SFIRE).

In this project, fire risk probability mapping, Prediction of fire points, and fire spread modelling will be carried out for the flammable forest areas in Australia. The fire risk probability model will be prepared by using the two-step Analytic Hierarchy Process (AHP) approach. The fire risk probability model will also be used for cross-validation of Support Vector Machine (SVM) model outputs. Then the Prediction of fire points will be done using SVM model (Linear kernel, Polynomial kernel, Radial kernel, sigmoid kernel) taking elevation, slope, aspect, Soil Moisture Layer (SML), Land Surface Temperature (LST), vegetation type layers for training the data set. Then the Weather Research and Forecasting (WRF) model will be used for obtaining meteorological data for the input of fire spread model using the Global Forecast System (GFS) as source data. After that, the fire spread path will be traced for various recent past fire event. The additional variables will be used for spread modelling other than SVM are, canopy cover, wind, temperature. The Cellular Automata (CA) fire spread model will setup in any two different locations of Australia.


Project Leader:
Sanjeev Kumar Srivastava, University of Sunshine Coast

PhD Student:
Harikesh Singh


Onboard Hyperspectral AI: Calibration, Panoptic Segmentation, Fine-grained Analysis, and joint space-ground inference

On-board high-performance computing

This project will develop brand new capabilities for onboard AI processing and analysis of hyperspectral imagery on smart satellite platforms. In particular, the project will tackle the key modules of calibration, coarse and fine-grained segmentation, and joint space-ground inference of onboard AI processing of hyperspectral data. New capabilities in these areas will transform the ability of a satellite to automatically make sense of the rich and multidimensional spectral modalities in an end-to-end manner onboard the satellite itself. This will create new opportunities to enable accurate, efficient, and reliable automated detection and classification of natural phenomena and human activities over a wide area on Earth.

At the heart of the project, the research team will develop a novel multi-task learning framework for hyperspectral data. This framework will be employed to create a Panoptic Segmentation network: an approach which unifies object detection, semantic segmentation and instance segmentation in a single network to simultaneously predict a dense pixel-level segmentation across multiple spectral channels from space. In addition to this, the project will develop a lightweight deep learning based atmospheric correction network which can also be deployed onboard; and explore how joint learning between satellite and ground-based sensors can be used to support the inference of detailed information in areas not covered by ground sensors.

This Phase 1 project will develop a proof-of-concept demonstrator system, developing the key techniques for later optimisation and integration to run onboard the Kanyini satellite (SASAT1).


Project Leader:
Professor Clinton Fookes, Queensland University of Technology


Cyber Security and Resilient Low Earth Orbit Satellite Operations: Development of Cyberworthiness using a Digital Twin Approach (CY-JAR)

Trusted Autonomous Satellite Operations

Technical failures on satellite systems can be difficult to attribute to cyberattacks. Often, there is insufficient information to support root cause analysis and space vehicles are unable to provide robust response options to cyber-attacks.

This project aims to support the development of cognisant satellites which are context aware and capable of increased mission resilience in the face of a contested cyber operating environment.


Project Leader:
Professor Jill Slay, University of South Australia


Compact Clock for Small Satellite Applications – Engineering Model

Quantum Clocks

This proposal is the second phase (of four phases), 18-month plan that will develop and test a TRL5 proof-of-concept, Engineering Model of a precision space-based clock. The project will bring together capabilities from QuantX Labs and the University of Adelaide with specific space-related consultancy support from SITAEL and the University of Melbourne.
This project aims to develop one of the key technologies that is essential for the development of a sovereign timing capability for Australia. It may also offer a replacement for technologies used in other jurisdictions because of its potential for higher performance than the current state-of-the-art.


Project Leader:
Martin O’Connor


Semi-Supervised Learning for Automatic Improvement of Onboard Object Detection Models on Small Satellites

Debris Avoidance

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.
We plan to develop a solution based on semi-supervised learning algorithms since they support improving a pretrained model using the unlabeled images captured by the satellite. Such a solution allows efficient use of the resources (e.g., hardware and data) available in orbit since the satellite system incorporates the following tasks: autonomously processing the captured input images to filter those of interest, transmitting them to ground stations for further analysis, and automatically improving the recognition performance of the onboard object detection model.
The focus of this project is to automatically increase the model’s accuracy in detecting relevant events or objects, which results in enhancing the quality of the transmitted data and, therefore, promoting faster human intervention for critical events. Furthermore, it to aims to create a generic solution with considerable potential to help various satellite applications that employ object detection algorithms regardless of the domain, e.g., environmental monitoring and agriculture.
The primary outcomes of this project consist of robust computational methods based on SSOD to enable the automatic improvement of object detection models on small satellites in orbit. Regarding academic results, when allowed by the SmartSat CRC, we expect to publish our findings in the form of scientific papers targeting top-tier international conferences and journals in computer vision, machine learning, and remote sensing topics.
Finally, we expect our work will also deliver practical outcomes such as open-source implementations of our methods, annotated datasets, and further cooperation with academic and industry partners of the SmartSat CRC.


Project Leader:
Associate Professor Gustavo Batista, the University of New South Wales

PhD Student:
Lucas Tsutsui da Silva