  Knowledge of tree species distribution is fundamental to managing forests.
New deep learning approaches promise significant accuracy gains for forest
mapping, and are becoming a critical tool for mapping multiple tree species at
scale. To advance the field, deep learning researchers need large benchmark
datasets with high-quality annotations. To this end, we present the PureForest
dataset: a large-scale, open, multimodal dataset designed for tree species
classification from both Aerial Lidar Scanning (ALS) point clouds and Very High
Resolution (VHR) aerial images. Most current public Lidar datasets for tree
species classification have low diversity as they only span a small area of a
few dozen annotated hectares at most. In contrast, PureForest has 18 tree
species grouped into 13 semantic classes, and spans 339 km$^2$ across 449
distinct monospecific forests, and is to date the largest and most
comprehensive Lidar dataset for the identification of tree species. By making
PureForest publicly available, we hope to provide a challenging benchmark
dataset to support the development of deep learning approaches for tree species
identification from Lidar and/or aerial imagery. In this data paper, we
describe the annotation workflow, the dataset, the recommended evaluation
methodology, and establish a baseline performance from both 3D and 2D
modalities.
