# Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

arXiv: https://arxiv.org/abs/2112.13583 · HTML: https://mlsanity.com/paper/2112.13583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans](https://mlsanity.com/paper/2204.11620/) (2022)
- [Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning](https://mlsanity.com/paper/2201.08051/) (2022)

## Abstract (arXiv)

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.