3 papers
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: A Decade of Tree-Level, Country-Scale Forest Monitoring
Martin Schwartz, Fajwel Fogel, Nikola Besic +9
The recent decline of the European forest carbon sink highlights the need for spatially explicit and frequently updated forest monitoring tools. Yet, existing satellite-based distu…
stat.AP2024
How reliable are remote sensing maps calibrated over large areas? A matter of scale?
Andrey Ramirez Luigui, Jean-Pierre Renaud, Cédric Vega
Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions…