论文标题
通过深度学习来预测空气传子数据的植被层占用
Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning
论文作者
论文摘要
我们提出了一种新的基于深度学习的方法，用于估计空气寄宿激光雷德点云中植被地层的占用。我们的模型预测了对应于较低，中，更高覆盖的三个植被地层的栅格化占用图。我们弱监督的训练计划允许我们的网络只能通过在包含数千点的圆柱图上汇总的植被占用值进行监督。这种地面真理比像素或点的注释更容易产生。我们的方法在精度上优于手工制作和深度学习的基线，同时提供了视觉和可解释的预测。我们提供开源实施，以及199个农业地块的数据集，以培训和评估弱监督的占用回归算法。
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. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted 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.
