The aim of the project is to maximise the repeatability of UAV survey results, whilst minimising data collection and processing requirements, through considering image characteristics within survey design.
The use of unpiloted aerial vehicles (UAVs) for image-based surveys is becoming increasingly widespread across the geosciences and in industries such as engineering and surveying. However, the increasing availability of such data has not been paralleled by an equivalent increase in our understanding of data quality. Consequently, data analysis and interpretation can be restricted by our limited knowledge of the magnitude of the uncertainties involved. This project will characterise the uncertainties in UAV imaging surveys and explore the implications for products such as digital elevation models and image orthomosaics.
The PhD research will exploit imaging and photogrammetric principles to assess the effect of these factors over a range of user scenarios and environments (e.g., geohazards, crop growth, infrastructure monitoring). The implications for 3D surface models (e.g. accuracy and precision), and for the use and analysis of orthomosaics (e.g. machine learning image classification) will be explored. The project will use the resulting advances in our understanding of photogrammetric processing performance for UAV datasets, to deliver efficiencies in UAV survey design.
The project will be based on a combination of existing and specifically-acquired UAV image-based surveys, and computer simulations. To enable data collection, the student will undergo training and certification as a UAV pilot and will also benefit from all the required training in photogrammetric image processing, data analysis and machine learning software.