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.

翻译：背景：挪威森林中的树种测绘是一项耗时的工作，涉及依赖专家手动标记的森林协会。该过程可结合航空影像、个人熟悉度、现场参考数据及遥感数据。当前最先进方法通常采用高分辨率航空影像与语义分割技术。方法：我们提出一种仅利用激光雷达数据的深度学习树种分类模型。通过基于U-Net的网络将激光雷达图像分割为四类（挪威云杉、欧洲赤松、桦树、背景）。模型使用焦点损失函数在部分弱标签数据上进行训练。该方法的主要优势在于激光雷达图像和标签底图均可免费开放获取。结果：在独立验证中，基于挪威国家森林资源清查实地样地数据，该树种分类模型的宏平均F1分数达0.70。该结果接近但仍低于仅使用航空影像或航空影像与激光雷达联合模型的性能水平。