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arXiv · 2112.13583

Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

Ekaterina Kalinicheva·Loic Landrieu·Clément Mallet·Nesrine Chehata
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Abstract

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.

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Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet, Nesrine Chehata. 2021-12-27. Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds. https://arxiv.org/abs/2112.13583

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