  We propose a new deep learning-based method for estimating the occupancy of
vegetation strata from 3D point clouds captured from an aerial platform. Our
model predicts rasterized occupancy maps for three vegetation strata: lower,
medium, and higher strata. Our training scheme allows our network to only being
supervized with values aggregated over cylindrical plots, which are easier to
produce than pixel-wise or point-wise annotations. Our method outperforms
handcrafted and deep learning baselines in terms of precision while
simultaneously providing visual and interpretable predictions. We provide an
open-source implementation of our method along along a dataset of 199
agricultural plots to train and evaluate occupancy regression algorithms.
