High-resolution tree counting and localization dataset in plain and hilly areas of eastern China

Trees are central organisms in maintaining global biodiversity and the health of the planet, and contribute extensively to biogeochemical cycles, and provide countless ecosystem services, including water quality control, wood stocks and carbon sequestration. Tree density is an important component of ecosystem structure, governing the rate of element processing and retention, as well as habitat suitability for many plant and animal species. The number of trees in a given area can also be a meaningful indicator to guide forest management practices and inform decision-making in public and governmental sectors. However, due to the complex distribution of trees, it has always been challenging to use remote sensing techniques to acquire it efficiently and effectively on a large spatial scale. In response to the growing need for individual trees scale research, we produce the Tree Counting Datasets based on GF-Ⅱ remote sensing images with a spatial resolution of 0.8m. This data set contains a total of 2400 samples in different geological scenarios in temperate and subtropical plains and hills, including wild woodland, urban and rural areas. Each sample pair consists of remote sensing images, tree annotation, and tree density maps generated by Gaussian convolution. The cross-validation experiment revealed the common counting networks could achieve the competitive performance (above 0.93) in terms of the determination coefficient (R²) between the ground truth and the estimated values and the average accuracy are greater than 84%. This dataset could be useful for tree density estimation, tree counting, and tree localization researches, thereby spurring biological analyses and facilitating model development for tasks that rely on individual tree prediction.树木是维持全球生物多样性与地球健康的核心生物类群，广泛参与生物地球化学循环，并提供诸多生态系统服务，涵盖水质调控、林木蓄积与碳封存。树木密度是生态系统结构的关键组成部分，其调控着元素的循环与留存速率，同时决定了众多动植物物种的栖息地适宜性。特定区域内的树木数量，亦是指导森林经营实践、为公共及政府部门决策提供科学依据的重要指标。然而受限于树木分布的复杂性，长期以来难以利用遥感技术在大空间尺度上高效且精准地获取该数据。为响应个体树木尺度研究日益增长的需求，我们基于空间分辨率为0.8米的高分二号（GF-Ⅱ）遥感影像构建了树木计数数据集。该数据集共计包含2400个样本，覆盖温带与亚热带平原、丘陵的不同地质场景，涵盖荒野林地、城乡区域。每组样本对均包含遥感影像、树木标注数据，以及通过高斯卷积生成的树木密度图。交叉验证实验结果显示，通用计数网络在实测真值与预测值的决定系数（R²）上可取得0.93以上的优异性能，平均准确率亦高于84%。本数据集可用于树木密度估计、树木计数与树木定位相关研究，以此推动生物学分析，并为依赖个体树木预测的各类任务的模型开发提供助力。