5 citations · 6 across the 3 of their papers we have counts for
4 papers
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…
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…
AI applications in forest monitoring need remote sensing benchmark datasets
Emily R. Lines, Matt Allen, Carlos Cabo +7
With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artifi…