Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning

Abstract

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.