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.
@article{arxiv.2112.13583,
title = {Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds},
author = {Ekaterina Kalinicheva and Loic Landrieu and Clément Mallet and Nesrine Chehata},
journal= {arXiv preprint arXiv:2112.13583},
year = {2021}
}