Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate change. The combination of high-resolution imagery from unmanned aerial vehicles and object detection by convolutional neural networks (CNNs) is one promising approach. However, unbiased assessments of these models and methods to integrate them into geospatial workflows are lacking. In this study, we present a method for rapid, large-scale mapping of young conifer seedlings using CNNs applied to RGB orthomosaic imagery. Importantly, we provide an unbiased assessment of model performance by using two well-characterised trial sites together containing over 30,000 seedlings to assemble datasets with a high level of completeness. Our results showed CNN-based models trained on two sites detected seedlings with sensitivities of 99.5% and 98.8%. False positives due to tall weeds at one site and naturally regenerating seedlings of the same species led to slightly lower precision of 98.5% and 96.7%. A model trained on examples from both sites had 99.4% sensitivity and precision of 97%, showing applicability across sites. Additional testing showed that the CNN model was able to detect 68.7% of obscured seedlings missed during the initial annotation of the imagery but present in the field data. Finally, we demonstrate the potential to use a form of weakly supervised training and a tile-based processing chain to enhance the accuracy and efficiency of CNNs applied to large, high-resolution orthomosaics.

绘制树苗的地图对于执行从监视自然演替和再生到有效的造林管理等任务非常有用。考虑到全球为减轻气候变化的影响而需要大量植树的方法，开发既准确又具有成本效益的方法尤为重要。来自无人机的高分辨率图像与通过卷积神经网络（CNN）进行目标检测的结合是一种很有前途的方法。但是，缺乏对将这些模型和方法集成到地理空间工作流程中的公正评估。在这项研究中，我们提出了一种用于CNNs的快速针叶树幼苗快速，大规模制图的方法，该CNN应用于RGB正马赛克图像。重要的，我们通过使用两个功能齐全的试验站点（共包含30,000多棵幼苗）来组装具有高度完整性的数据集，从而对模型性能进行了公正的评估。我们的结果表明，在两个站点上训练的基于CNN的模型检测到的幼苗的敏感性分别为99.5％和98.8％。一处杂草丛生，同一物种的自然再生幼苗导致假阳性，导致精度略低，分别为98.5％和96.7％。在两个站点的示例上训练的模型的灵敏度为99.4％，精度为97％，显示了跨站点的适用性。进一步的测试表明，CNN模型能够检测出在图像最初标注期间遗漏但存在于野外数据中的68.7％的模糊幼苗。最后，