🌲 Forest and trees workflow was updated with two models, both benchmarked against thoroughful manual labels.

🚀 Tree crowns, v.2026-09-10

Crown polygons re-evaluated on 7 AOIs against manually annotated crowns:

mean F1 0.824 → 0.849

Neighbouring canopies are separated instead of merged — which is what tree counting actually depends on.
Run it with the Tree crowns option on 0.3 m imagery (zoom 19): polygons, or points.

🚀 Canopy Height Model, v.2026-08-31

Benchmarked against lidar-derived ground truth on 5 m hexagons. Across 38 AOIs the mean absolute height error drops from 4.70 m to 3.24 m (−31%).

Better heights mean a more reliable split into shrubs <4 m / forest 4–10 m / forest >10 m — the ranges used for vegetation management along power lines and transport corridors, which is customizable.

📖 Read more in our docs:
📊 Per-location benchmarks, with prediction-vs-ground-truth overlays:
1) Crowns 👈 2) CHM 👈