Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

• URL: http://arxiv.org/abs/2112.13583v1
• Date: Mon, 27 Dec 2021 09:33:08 GMT
• Title: Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
• Authors: Ekaterina Kalinicheva, Loic Landrieu, Cl\'ement Mallet, Nesrine Chehata
• Abstract summary: 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 network is 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.
• Score: 5.7047887413125276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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