Lidar-based Norwegian tree species detection using deep learning

• URL: http://arxiv.org/abs/2311.06066v1
• Date: Fri, 10 Nov 2023 14:01:05 GMT
• Title: Lidar-based Norwegian tree species detection using deep learning
• Authors: Martijn Vermeer and Jacob Alexander Hay and David V\"olgyes and Zs\'ofia Koma and Johannes Breidenbach and Daniele Stefano Maria Fantin
• Abstract summary: We present a deep learning based tree species classification model utilizing only lidar data. The model is trained with focal loss over partial weak labels. Our model achieves a macro-averaged F1 score of 0.70 on an independent validation.
• Score: 0.36651088217486427
• License: http://creativecommons.org/licenses/by/4.0/
• 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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