activity
20182021
most citedDevelopment of a High Fidelity Simulator for Generalised Photometric Based Space Object Classification using Machine Learning

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20211 cited

A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees

Fredrik Westling, James Underwood, Mitch Bryson

In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth. Due to the slow nature of ag…

eess.IV2020

SimTreeLS: Simulating aerial and terrestrial laser scans of trees

Fredrik Westling, Mitch Bryson, James Underwood

There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of…

cs.CV2020

Graph-based methods for analyzing orchard tree structure using noisy point cloud data

Fredrik Westling, Dr James Underwood, Dr Mitch Bryson

Digitisation of fruit trees using LiDAR enables analysis which can be used to better growing practices to improve yield. Sophisticated analysis requires geometric and semantic unde…

physics.space-ph20202 cited

Development of a High Fidelity Simulator for Generalised Photometric Based Space Object Classification using Machine Learning

James Allworth, Lloyd Windrim, Jeffrey Wardman +3

This paper presents the initial stages in the development of a deep learning classifier for generalised Resident Space Object (RSO) characterisation that combines high-fidelity sim…

cs.RO2018

Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR

Lloyd Windrim, Mitch Bryson

This paper presents an autonomous approach to tree detection and segmentation in high resolution airborne LiDAR that utilises state-of-the-art region-based CNN and 3D-CNN deep lear…