Optimising solar farm usage utilising aerial images
The PhD will use historical and current ground-based data and observations to validate and train a machine learning model to obtain the same information from high resolution hyperspectral satellite images.
This project aims to leverage high resolution satellite images and artificial intelligence to enhance vegetation management and grazing efficiency in solar farms.
Key details
- 20246658
- MRes Year 2 + PhD, PhD
- Applications close on 30 April 2025
- Domestic
- Engineering
- $47,000 p.a.
The potential benefits are enhanced solar operation and vegetation management efficiency, bush fire risk monitoring and solar grazing.
The main objectives are to:
- develop experiments to collect field training and validation data that can be associated with concurrent satellite data
- investigate a range of available satellite imagery, including free and commercial data, to determine the most appropriate technology for the application, including determining the acceptable degree of accuracy and uncertainty
- develop algorithms using the most appropriate satellite imagery to monitor and predict key factors including curing rates, vegetation growth, grazing patterns that can be used to determine the effectiveness of land management
- validate the satellite-derived measurements with real-world data.
Availability
This scholarship is available to eligible candidates to undertake:
- a direct entry three-year PhD program, or
- a four-year MRes Year 2 + PhD program.
Components
The scholarships comprise:
- a tuition fee offset/scholarship
- a living allowance stipend
- supervision by the participating university, CSIRO and an industry partner
- a project Expense and Development package of $13,000 per annum
- a three month industry engagement component with the industry partner
- a structured professional development and training program to develop your applied research skills.
The value of each stipend scholarship is $47,000 per annum (full-time, fixed rate) for four years.