From above, a tree is a crown — a roughly circular blob of green, distinct from its neighbors by a gap or a color difference. From below, a tree is a trunk that disappears into a chaotic canopy, partially occluded by other trunks, understory shrubs, and variable light filtering through leaves. The same tree, two completely different perception problems.
SilvaScenes (arXiv:2510.09458) provides 1,476 individually annotated trees from 24 species across five bioclimatic domains in Quebec, all from under-canopy ground-level imagery. The dataset exposes a structural asymmetry: tree detection achieves 67.7% mean average precision, but species classification drops to 35.7%. Finding a tree is manageable. Identifying what species it is — from a ground-level view through heavy occlusion, variable lighting, and dense vegetation — is genuinely hard.
The asymmetry reveals what information survives perspective change. Aerial views preserve crown shape, leaf color, canopy texture — features that differ between species. Ground views preserve trunk diameter, bark texture, branching pattern — features that vary enormously within species depending on age, damage, and growing conditions. The features that are diagnostic from above are invisible from below, and the features visible from below are unreliable.
This matters for forestry robotics. An autonomous inventory system operating under the canopy can locate trees efficiently but can't reliably identify them. The bottleneck isn't computation or model architecture. It's the information content of the viewpoint itself.