FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Reported by arXiv cs.AI: FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data. Analysis and context written by TickrWire.
FLORA introduces a deep learning model to predict forest attributes from heterogeneous LiDAR data, addressing challenges posed by varying sensors, flight parameters, and environmental conditions in national-scale forest monitoring.
Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes used in National Forest Inventory (NFI) estimates. However, producing wall-to-wall predictions remains challenging when LiDAR data are acquired under heterogeneous conditions. As national LiDAR programs expand across Europe, variability in sensors, flight parameters, seasons, and scan angles limits the robustness of existing models, which are often calibrated for local conditions. We present FLORA (Forest LiDAR Octree R
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