Lidar-based Norwegian tree species detection using deep learning

Martijn Vermeer, Jacob Alexander Hay, David Völgyes, Zsofia Koma, Johannes Breidenbach, Daniele Fantin

Keywords: deep learning, u-net, semantic segmentation, forest, lidar

Abstract:

Background:
The mapping of tree species within Norwegian forests is a time-consuming process,
involving forest associations relying on manual labeling by experts. The process
can involve aerial imagery, personal familiarity, on-scene references,
and remote sensing data. The state-of-the-art methods usually use high-resolution
aerial imagery with semantic segmentation methods.

Methods: We present a deep learning based tree species classification model
utilizing only lidar (Light Detection And Ranging) data.
The lidar images are segmented into four classes (Norway Spruce, Scots Pine, Birch, background)
with a U-Net based network. The model is trained with focal loss over
partial weak labels. A major benefit of the approach is that both the lidar imagery and
the base map for the labels have free and open access.

Results: Our tree species classification model achieves a macro-averaged $\mathrm{F}_1$ score
of 0.70 on an independent validation with National Forest Inventory (NFI) in-situ sample plots.
That is close to, but below the performance of aerial, or aerial and lidar combined models.

Submission Number: 37