5 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS
Matthew J. Allen, Harry J. F. Owen, Stuart W. D. Grieve +1
Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based f…
PointsToWood: A deep learning framework for complete canopy leaf-wood segmentation of TLS data across diverse European forests
Harry J. F. Owen, Matthew J. A. Allen, Stuart W. D. Grieve +2
Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function but typically require extensive manual proce…
Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation
M. J. Allen, D. Moreno-Fernández, P. Ruiz-Benito +2
The global increase in observed forest dieback, characterised by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant cha…
Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset
Stefano Puliti, Emily R. Lines, Jana Müllerová +30
Proximally-sensed laser scanning offers significant potential for automated forest data capture, but challenges remain in automatically identifying tree species without additional…