Abstract
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.
Abstract (translated)
树木物种分布的了解是管理森林的基础。新的深度学习方法预计将在森林制图方面显著提高准确性，并成为放大规模绘制多种树种的 critical 工具。为了推动该领域的发展，深度学习研究人员需要大型高质量标注的数据集。为此，我们提出了 PureForest 数据集：一个大规模、开放、多模态的数据集，旨在从空域激光扫描（ALS）点云和非常高分辨率（VHR）航空图像中对树木物种进行分类。目前，大多数公开的 Lidar 数据集用于树木物种分类时具有较低的多样性，因为它们只覆盖了少数 annotated 公顷的土地。相比之下，PureForest 把 18 种树木分成了 13 个语义类，跨越了 449 个不同的单一森林，迄今为止是最大的、最全面的 Lidar 数据集，用于识别树木物种。通过将 PureForest 公开发布，我们希望为开发从 Lidar 和/或航空影像中识别树木物种的深度学习方法提供一个具有挑战性的基准数据集。在本文的数据论文中，我们描述了标注工作流程、数据集、推荐的评估方法和从 3D 和 2D 模态中建立基准性能。
URL
https://arxiv.org/abs/2404.12064
