This repository contrains code and dataset for the article "Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans", CVPR 2022, Earth Vision Workshop.

The WildForest3D dataset contains 29 plots of dense forest, with 7 million 3D points and 2.1 million individual labels. The study area is located in the heavily forested French region of Aquitaine, and was scanned using a LiDAR installed on unmanned aerial vehicle with an average of 60 pulses per m². Each point is associated with its coordinates in Lambert-93 projection, the intensity value of returned laser signal, and the echo return number. The elevation of the points is given using a digital elevation model, such that the ground points are always at z=0m.

The dataset is located in WildForest3D folder. The dataset structure is the following:

• data_point_clouds - contains 29 folders Placette_XX organized by plot ID, each of these folders contains *.ply point clouds and corresponding *.csv files with the parameters of each individual tree/bush instance. The *.ply file contains clipped plots with annotated point clouds, the points are annotated in an instance-wise way, so that each point contains the following information : XYZ coordinates, intensity value, echo return number, and the ID of the instance the point belongs to. ID=0 corresponds to a non-annotated point. The information about each instance (its class, height, crown base height, etc) is contained in the *.csv files. Pl_XX_final_trees.csv contains the original tree class codes, while in Pl_XX_final_trees_cat.csv those classes transformed into categories so it can be usable for model training. Using the script utils/open_ply_all.ply we can generate the 6 classes dataset that was used in the article.
• stratum_coverage_2D - folder with GeoTIFF ground truth rasters generated from 3D point clouds (see the article)