Reconstructing land tenure maps of Australia in 3D. Existing land tenure maps of above and below ground, such as apartment ownership, tunnels, and mining, are maintained using 2D drawings. However, the drawings are not structured and valuable for detailed and advanced visualisation, analytics, and simulation, which are essential for testing potential interventions and policy development. This project aims to develop a data validation framework for transforming current drawings and reconstructing ....Reconstructing land tenure maps of Australia in 3D. Existing land tenure maps of above and below ground, such as apartment ownership, tunnels, and mining, are maintained using 2D drawings. However, the drawings are not structured and valuable for detailed and advanced visualisation, analytics, and simulation, which are essential for testing potential interventions and policy development. This project aims to develop a data validation framework for transforming current drawings and reconstructing them into 3D models. The outcomes include validation principles, formal mathematical terms, and computational algorithms. Benefits include a cost-effective onshore alternative to offshore 3D reconstruction practices, efficient land development and infrastructure planning, and fewer property disputes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100094
Funder
Australian Research Council
Funding Amount
$430,000.00
Summary
A new 3D data model to integrate underground land information in Australia. This project aims to develop a novel 3D digital approach to managing subterranean ownership spaces by referencing these spaces to the physical reality of the underground environment. This project expects to generate new knowledge in the area of underground land administration using new 3D data modelling techniques. Expected outcomes of this project include a new underground 3D data model to improve management and communi ....A new 3D data model to integrate underground land information in Australia. This project aims to develop a novel 3D digital approach to managing subterranean ownership spaces by referencing these spaces to the physical reality of the underground environment. This project expects to generate new knowledge in the area of underground land administration using new 3D data modelling techniques. Expected outcomes of this project include a new underground 3D data model to improve management and communication of physical location and ownership extent of Australia’s underground assets. This should provide significant benefits such as protecting underground assets, decreasing the risk of damaging utilities, avoiding unnecessary disruptions and delays when planning, constructing and managing underground infrastructure. Read moreRead less
Walking the city: Digital infrastructure for pedestrian mobility. Pedestrian access, flow and management are critical for urban life. However, compared to other forms of mobility pedestrian mobility is significantly more complex. Currently, various incompatible pedestrian route graphs in both outdoor and indoor environments render any analysis biased and non-transparent. This project aims to solve this problem by developing a universal and necessarily hierarchical pedestrian route graph to suppo ....Walking the city: Digital infrastructure for pedestrian mobility. Pedestrian access, flow and management are critical for urban life. However, compared to other forms of mobility pedestrian mobility is significantly more complex. Currently, various incompatible pedestrian route graphs in both outdoor and indoor environments render any analysis biased and non-transparent. This project aims to solve this problem by developing a universal and necessarily hierarchical pedestrian route graph to support critical applications such as urban walkability (health), space and asset management (guidance, flow management), and public safety (evacuation). In contrast to conventional algorithms, we will take a novel approach based on human cognition to define this universal graph and then integrate topology and geometry.Read moreRead less
Big data modelling to forecast crop yield to enable precision fertilisation. This project aims to lay a foundation for a generic data-driven approach to more precise management of our agricultural landscapes. A multitude of agriculture-related data streams are now available to growers to characterise their yield, management, soil and weather. However, currently there is no approach able to digest all these disparate data streams to enable a management decision. The project will develop an appro ....Big data modelling to forecast crop yield to enable precision fertilisation. This project aims to lay a foundation for a generic data-driven approach to more precise management of our agricultural landscapes. A multitude of agriculture-related data streams are now available to growers to characterise their yield, management, soil and weather. However, currently there is no approach able to digest all these disparate data streams to enable a management decision. The project will develop an approach to harness all of these data streams to guide spatially variable applications of nitrogen fertilisers with a focus on grains cropping. This should provide the opportunity to allocate fertiliser inputs as required at fine spatial scales according to local soil and weather conditions to maximise profit and minimise off-farm impacts of excessive fertilisation.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH210100048
Funder
Australian Research Council
Funding Amount
$4,980,000.00
Summary
ARC Industry Transformation Research Hub for Resilient and Intelligent Infrastructure Systems (RIIS) in Urban, Resources and Energy Sectors. RIIS will deliver transformational technologies to address Australia’s critical infrastructure needs. It will integrate advances in sensor technology, connectivity, data analytics, machine learning, robotics, smart materials, and reliable models to deliver resilient and adaptive infrastructure systems in urban, energy and resources sectors. All three sector ....ARC Industry Transformation Research Hub for Resilient and Intelligent Infrastructure Systems (RIIS) in Urban, Resources and Energy Sectors. RIIS will deliver transformational technologies to address Australia’s critical infrastructure needs. It will integrate advances in sensor technology, connectivity, data analytics, machine learning, robotics, smart materials, and reliable models to deliver resilient and adaptive infrastructure systems in urban, energy and resources sectors. All three sectors are critical to Australia's prosperity and well-being. It will engage with industry, government, and community to unlock scientific roadblock, deliver foundational skills, and translate research and development to commercial opportunities. Benefits include: improved productivity, competitiveness, resiliency, safety; growth, job creation; technological leadership, and export potential.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE240100131
Funder
Australian Research Council
Funding Amount
$539,000.00
Summary
Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boo ....Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boost Australia’s pivotal research leadership and business competitiveness in nurturing new-generation, collaborative and transformative digital R&D and talent pipeline. It will enable large-scale strategic business innovation and transformation including smart manufacturing and Industry 4.0.Read moreRead less
Special Research Initiatives - Grant ID: SR200200227
Funder
Australian Research Council
Funding Amount
$277,000.00
Summary
Indigenous Engineering: interpreting engineering foundations of Budj Bim. The Budj Bim World Heritage Cultural Landscape is internationally recognised for preserving the world’s oldest aquaculture system, which provided an economic and social base for the Gunditjmara people of South-western Victoria for more than six millennia. This project aims to elucidate the engineering processes that enabled the Gunditjmara to site, plan, construct, operate and maintain this aquaculture complex, to show how ....Indigenous Engineering: interpreting engineering foundations of Budj Bim. The Budj Bim World Heritage Cultural Landscape is internationally recognised for preserving the world’s oldest aquaculture system, which provided an economic and social base for the Gunditjmara people of South-western Victoria for more than six millennia. This project aims to elucidate the engineering processes that enabled the Gunditjmara to site, plan, construct, operate and maintain this aquaculture complex, to show how it may have evolved over time, and how it responded to changing social and environmental circumstances. This project will develop geospatial methods to uncover and document the technological foundations of the aquaculture complex, and contribute to the understanding of the Gunditjmara technological knowledge and history. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101033
Funder
Australian Research Council
Funding Amount
$420,154.00
Summary
Scalable and Lightweight On-Device Recommender Systems. This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and mod ....Scalable and Lightweight On-Device Recommender Systems. This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage.Read moreRead less
Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical cluste ....Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical clustering algorithms for dynamic networks – with performance guarantees of efficiency and clustering quality – and prototype software, guiding us to pick a good clustering. Expected benefits include better understanding of spread in evolving social networks, accelerating the software testing cycle, and improved topic detection.Read moreRead less
Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. ....Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.Read moreRead less