TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds

Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander Ecker · Ecological Informatics 2024 · 2024

DOI arXiv Code

Status

To read

Topics

forest-segmentation

Feeds

P3 · Q1 journal

Dataset

TreeLearn dataset

What they did

Presents TreeLearn, a deep learning method for segmenting individual trees in ground-based LiDAR forest point clouds, trained on already segmented point clouds instead of relying on hand-crafted rules.

My note

No note yet.

Code

Repository

ecker-lab/TreeLearn

Framework

PyTorch

Licence

MIT

My status

Not tried

BibTeX

@article{henrich2024-treelearn,
  title = {{TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds}},
  author = {Jonathan Henrich and Jan van Delden and Dominik Seidel and Thomas Kneib and Alexander Ecker},
  journal = {Ecological Informatics 2024},
  year = {2024},
  doi = {10.1016/j.ecoinf.2024.102888},
  eprint = {2309.08471},
  archivePrefix = {arXiv}
}