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.

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).

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.

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.

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.

The project will focus on detecting anomalous traffic within SDN-based smart satellite networks using Deep Learning (DL). As IoT devices are growing at an exponential rate, so does the threats and vulnerabilities faced by IoT. As the world is migrating to the Fourth Industrial Revolution (4IR), there will be a heightened growth of IoT systems that will be required to maintain uninterrupted connectivity on a large scale. The need for early detection of cyber-attacks that target smart satellite networks based IoT systems is evident. Additionally, DL can analyse complex and large traffic data patterns in the least amount of time and is therefore being adopted in areas of health, mining, and agriculture amongst others. The project will contribute to SmartSat’s focus areas of Advanced Satellite Systems, Sensors and Intelligence specifically the data security of satellite systems, and the Security of Satellite-based IoT services with Deep Reinforcement Learning and is in close association with SmartSat CRC’s Project P2-36, Cyber Jeopardy and Response (CY-JAR).