We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points which are typically easier to produce than pixel-wise or point-wise annotations. We propose to employ a deep neural network operating on 3D points, and whose prediction are projected onto rasters representing the different vegetation strata. Our method outperforms handcrafted, regression and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms.

我们提出了一种新的基于深度学习的方法，用于从机载 3D LiDAR 点云中估计植被层的占用率。我们的模型预测了对应于低、中和高覆盖的三个植被层的栅格化占用图。我们的弱监督训练方案允许我们的网络仅使用在包含数千个点的圆柱形图上聚合的植被占用值进行监督，这些点通常比逐像素或逐点注释更容易产生。我们建议使用在 3D 点上运行的深度神经网络，并将其预测投影到代表不同植被层的栅格上。我们的方法在精度方面优于手工、回归和深度学习基线高达 30%，同时提供视觉和可解释的预测。