FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Emilie Vautier, Clément Mallet, Cédric Vega
• Venue: arXiv: cs.CV
• Published: 2026-06-30
• Collected: 2026-07-01
• URL: http://arxiv.org/abs/2606.32023v1
• PDF: https://arxiv.org/pdf/2606.32023v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers present FLORA, a deep learning framework that uses LiDAR data and auxiliary variables to predict six forest attributes in mainland France. The model outperforms season-specific models and achieves an rRMSE of about 12.3% for dominant height and 39% for total volume.

What the Document Says

• The research is about a deep learning framework named FLORA which predicts forest attributes using LiDAR point clouds and auxiliary variables.
• FLORA combines an octree-based backbone with ecological and spatiotemporal auxiliary variables through a late-fusion gating mechanism.
• The models are trained and evaluated on 32,052 National Forest Inventory plots in mainland France using data from the French LiDAR HD program.
• FLORA predicts six forest attributes: dominant height, total volume, deciduous volume, coniferous volume, basal area, and stem density.

Key Concepts Mentioned

• deep learning
• LiDAR point clouds
• forest attributes
• national-scale resource monitoring
• heterogeneous LiDAR data
• Octree Regression

Claims Made by the Document

• FLORA outperforms season-specific models.
• FLORA improves cross-season robustness.

Evidence Used

• The text mentions that a single model trained on both leaf-on and leaf-off acquisitions outperforms season-specific models.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 Regression with Auxiliary Data), a deep learning framework that predicts six forest attributes: dominant height, total volume, deciduous volume, coniferous volume, basal area, and stem density from heterogeneous LiDAR point clouds. FLORA combines an octree-based backbone with ecological and spatiotemporal auxiliary variables through a late-fusion gating mechanism. Models are trained and evaluated on 32,052 National Forest Inventory plots across mainland France using data from the French LiDAR HD program. A single model trained on both leaf-on and leaf-off acquisitions outperforms season-specific models and improves cross-season robustness. Auxiliary variables provide modest overall gains but contribute more strongly to species-specific volume prediction. FLORA achieves an rRMSE of about 12.3% (R2 = 0.88) for dominant height and 39% (R2 = 0.74) for total volume, providing a robust baseline for large-scale forest attribute estimation from heterogeneous national LiDAR programs.