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cs.CV2026
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Emilie Vautier, Clément Mallet, Cédric Vega
Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes use…
cs.CV2025
FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series
Martin Schwartz, Fajwel Fogel, Nikola Besic +9
Current large-scale satellite-based forest disturbance monitoring systems operate at 10-30~m resolution, too coarse to detect changes at the scale of individual trees and resulting…