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arXiv · 2311.06066

Lidar-based Norwegian tree species detection using deep learning

Martijn Vermeer·Jacob Alexander Hay·David Völgyes·Zsófia Koma·Johannes Breidenbach·Daniele Stefano Maria Fantin
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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 both aerial imagery, personal familiarity, or 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 F1 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.

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Martijn Vermeer, Jacob Alexander Hay, David Völgyes, Zsófia Koma, Johannes Breidenbach, Daniele Stefano Maria Fantin. 2023-11-10. Lidar-based Norwegian tree species detection using deep learning. https://arxiv.org/abs/2311.06066

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