# Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

arXiv: https://arxiv.org/abs/2204.11620 · HTML: https://mlsanity.com/paper/2204.11620/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles](https://mlsanity.com/paper/2103.03975/) (2021)
- [A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data](https://mlsanity.com/paper/1701.00198/) (2017)
- [Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds](https://mlsanity.com/paper/1702.06188/) (2017)
- [Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds](https://mlsanity.com/paper/2112.13583/) (2021)
- [Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning](https://mlsanity.com/paper/2201.08051/) (2022)

## Built on by (most cited first, 6 of 6)

- [Open-Canopy: Towards Very High Resolution Forest Monitoring](https://mlsanity.com/paper/2407.09392/) (2024)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans](https://mlsanity.com/paper/2304.09704/) (2023)
- [Benchmarking Individual Tree Mapping with Sub-meter Imagery](https://mlsanity.com/paper/2311.07981/) (2023)
- [Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring](https://mlsanity.com/paper/2405.00514/) (2024)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)

## Abstract (arXiv)

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ekalinicheva/multi_layer_vegetation.