Airborne spatial tracking to save endangered species. Airborne spatial tracking to save endangered species. This project aims to develop an automated and distributed spatial tracking approach using low cost Unmanned Aerial Vehicles (UAVs) to locate and study endangered wildlife. Understanding animal behaviour and habits with granular spatial data is essential to develop effective monitoring and conservation strategies. Spatial tracking of radio collared wildlife using radio telemetry is a critic ....Airborne spatial tracking to save endangered species. Airborne spatial tracking to save endangered species. This project aims to develop an automated and distributed spatial tracking approach using low cost Unmanned Aerial Vehicles (UAVs) to locate and study endangered wildlife. Understanding animal behaviour and habits with granular spatial data is essential to develop effective monitoring and conservation strategies. Spatial tracking of radio collared wildlife using radio telemetry is a critical but costly tool for acquiring this data. This project anticipates that airborne spatial tracking using intelligent spatial tracking algorithms on board low cost UAV teams will allow more precise understanding of wildlife for evidence-based conservation and management in a changing global climate.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.