Towards Autonomy for Unoccupied Aerial Systems in UN SDGs

This project will use a number of case studies of forest stress (e.g., liana infestation captured using unoccupied aerial systems (UAS)) from around the World to exploit the power of AI on remotely captured data to detect the source of stress of stress. The power of hyperspectral and/or hyperspatial and/or hypertemporal remotely captured data will then be used to develop autonomous remote sensing for forest monitoring. The learnings from forestry will be explored in the context of the wider UN Global Goals.