用无人机影像同时估测单棵树高度与树种，精度高且模型更轻量。
Estimating Individual Tree Height and Species from UAV Imagery
- 基于视觉基础模型构建统一框架，联合预测树高与树种。
- 在三个森林类型数据集上实现高精度树高估计与良好分类效果。
- 模型参数量仅为次优方案的54%~58%，适合实际部署。
准确估算森林生物量（主要碳汇）依赖于树高和树种等个体层面特征。利用仅搭载单个RGB相机的无人飞行器（UAV）获取高分辨率影像，为个体树木的制图与测量提供了低成本、可扩展的解决方案。本文提出BIRCH-Trees，首个基于树心视角无人机影像的个体树高与树种估计基准数据集，涵盖温带森林、热带森林及寒带人工林三类场景。同时提出DINOvTree，一种采用视觉基础模型（VFM）主干网络并配备任务专用头的统一方法，实现树高与树种的联合预测。在BIRCH-Trees上的广泛评估表明，DINOvTree在树高预测上表现最佳，分类准确率具有竞争力，且仅需次优方案54%至58%的参数量。
原文摘要 · Abstract (English)
Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.
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