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Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

Authors :
Kalinicheva, Ekaterina
Landrieu, Loic
Mallet, Clément
Chehata, Nesrine
Source :
SilviLaser 2021 Conference
Publication Year :
2021

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.

Details

Database :
arXiv
Journal :
SilviLaser 2021 Conference
Publication Type :
Report
Accession number :
edsarx.2112.13583
Document Type :
Working Paper