Reimagining Precision Forestry with Mobile Laser Scanning and AI Integration

Our capacity to map forests in three dimensional space and get a deeper understanding of forest ecosystems is being transformed by mobile laser scanning (MLS) and computer vision. Imagine being able to efficiently obtain vital information about above-ground biomass while also collecting detailed data on individual trees, even down to the smallest branches. High-resolution 3D point clouds produced by MLS using   simultaneous localisation and mapping algorithms have enormous potential for scaling forestry applications, even though they do not yet match terrestrial laser scanning level of noise and accuracy. 

The focus of this PhD project will be on the investigation of the transformational potential of combining MLS with AI. We want to create novel data collection techniques that optimise accuracy and usability by testing both low-cost prototypes and cutting-edge MLS devices in order to directly measure above-ground biomass. 

We willll create a robust data processing pipeline, integrating cutting-edge deep learning models for image denoising and 3D semantic segmentation of individual trees. This model will link to quantitative structure models or alternative methods to estimate above-ground biomass.

Our ultimate goal? A comprehensive workflow taking you from field data collection to actionable results—empowering applications in national forest inventories and precision forestry. Join us to make MLS the future of forestry that will be smarter, faster, and more accessible.