This PhD project will develop and test innovative methods for automatically detecting archaeological features using drone-based photography and artificial intelligence. Focusing on the vast desert landscapes of Australia, and collaborating with our industry partners, the research will explore how high-resolution aerial imagery can be analysed using computer vision and machine learning to identify archaeological features across large survey areas. A primary target will be the detection of stone artefact scatters, one of the most widespread and informative forms of archaeological evidence in arid Australia. The project will also investigate the automatic identification of other archaeological landscape features visible in aerial imagery. Australia’s desert regions contain extensive archaeological records but are challenging to survey using traditional ground-based methods. By developing automated detection approaches, this project aims to significantly increase the scale and efficiency of archaeological survey. The research has the potential to transform how archaeological landscapes are identified and monitored, support improved cultural heritage management, assisting Indigenous communities in protecting cultural sites, and enabling a more effective archaeological assessment framework for industry and land disturbance activities. Through testing multiple automated detection approaches, including advanced computer vision and machine learning techniques, the project will help establish new digital methods for large-scale archaeological survey in arid environments.
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