TreeLearn
Segments individual trees from ground-based and mobile lidar scans
- Field
- Forestry & vegetation
- Method
- Deep learning
- Data it takes
- point-cloud
- License
- MIT
TreeLearn segments individual trees from terrestrial and mobile laser scans, where the scanner sits beneath the canopy rather than above it. It predicts an offset vector for each point toward its tree’s center, then clusters, an approach suited to the dense and occluded geometry a ground-based scan produces.
Ground-based scanning resolves stems and branch structure not visible to airborne lidar. TreeLearn is the counterpart to SegmentAnyTree rather than a competitor.
Catalog entry last checked 2026-08-17. All models →
Run TreeLearn on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
Contact the Lab