用高斯模型识别全球3米分辨率影像中的单棵树。
Trees as Gaussians: Large-Scale Individual Tree Mapping
- 用可变大小的高斯核模拟树冠，提取树中心和树覆盖图。
- 在空中激光雷达上验证，树覆盖率预测相关系数达0.81。
- 适合需要高精度个体树木监测的研究与生态保护应用。
树木是陆地生物圈的关键组成部分，在生态系统功能、气候调节和生物经济中发挥重要作用。然而，大范围个体树木监测仍受限于建模能力不足。现有全球产品多聚焦于二值树覆盖或冠层高度，未能显式识别个体树木。本研究提出一种深度学习方法，基于3米分辨率的PlanetScope影像，在全球尺度上检测大型个体树木。通过使用可扩展尺寸的高斯核模拟树冠，实现树冠中心提取与二值树覆盖图生成。训练数据来自从机载激光雷达自动提取的数十亿个点，使模型能有效识别林内与林外树木。与现有树覆盖图及机载激光雷达对比，表现达到领先水平（与空中激光雷达的覆盖率相关系数R²=0.81），在不同生物群落中保持均衡检测性能，并展示通过人工标注微调可进一步提升效果。该方法为全球高分辨率树木监测提供可扩展框架，且适用于未来提供更优影像的卫星任务。
原文摘要 · Abstract (English)
Trees are key components of the terrestrial biosphere, playing vital roles in ecosystem function, climate regulation, and the bioeconomy. However, large-scale monitoring of individual trees remains limited by inadequate modelling. Available global products have focused on binary tree cover or canopy height, which do not explicitely identify trees at individual level. In this study, we present a deep learning approach for detecting large individual trees in 3-m resolution PlanetScope imagery at a global scale. We simulate tree crowns with Gaussian kernels of scalable size, allowing the extraction of crown centers and the generation of binary tree cover maps. Training is based on billions of points automatically extracted from airborne lidar data, enabling the model to successfully identify trees both inside and outside forests. We compare against existing tree cover maps and airborne lidar with state-of-the-art performance (fractional cover R$^2 = 0.81$ against aerial lidar), report balanced detection metrics across biomes, and demonstrate how detection can be further improved through fine-tuning with manual labels. Our method offers a scalable framework for global, high-resolution tree monitoring, and is adaptable to future satellite missions offering improved imagery.
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