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We propose a new deep learning-based method for estimating the occupancy ofvegetation strata from airborne 3D LiDAR point clouds. Our model predictsrasterized occupancy maps for three vegetation strata corresponding to lower,medium, and higher cover. Our weakly-supervised training scheme allows ournetwork to only be supervised with vegetation occupancy values aggregated overcylindrical plots containing thousands of points. Such ground truth is easierto produce than pixel-wise or point-wise annotations. Our method outperformshandcrafted and deep learning baselines in terms of precision by up to 30%,while simultaneously providing visual and interpretable predictions. We providean open-source implementation along with a dataset of 199 agricultural plots totrain and evaluate weakly supervised occupancy regression algorithms.

Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning