The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds
Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomass estimation. Existing segmentation methods typically exclude radiometric features (e.g., intensity, return number) to maximize cross-sensor compatibility. We challenge this design choice by evaluating cross-site and cross-platform generalization: training on the public Heidelberg dataset (terrestrial TLS, 1550nm) and testing on a novel dataset from Ontario, Canada (RPA-LS, 905nm). Results show that geometry-only methods - including state-of-the-art deep learning models trained on high-density LiDAR datasets - fail to generalize to the spar...
| Authors | Roman Kaharlytskyi, Derek T. Robinson, Roberto Guglielmi |
|---|---|
| Primary category | cs.CV |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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