Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
This work addresses vegetation monitoring for agricultural or environmental applications, but it is incremental as it builds on existing deep learning approaches with a specific annotation scheme.
The paper tackles the problem of predicting vegetation stratum occupancy from airborne LiDAR 3D point clouds by proposing a deep learning method that uses aggregated cylindrical plot annotations, and it outperforms baselines in precision while providing interpretable predictions.
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.