Forest biomass is a key influence for future climate, and the world urgently
needs highly scalable financing schemes, such as carbon offsetting
certifications, to protect and restore forests. Current manual forest carbon
stock inventory methods of measuring single trees by hand are time, labour, and
cost-intensive and have been shown to be subjective. They can lead to
substantial overestimation of the carbon stock and ultimately distrust in
forest financing. The potential for impact and scale of leveraging advancements
in machine learning and remote sensing technologies is promising but needs to
be of high quality in order to replace the current forest stock protocols for
certifications.
In this paper, we present ReforesTree, a benchmark dataset of forest carbon
stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we
show that a deep learning-based end-to-end model using individual tree
detection from low cost RGB-only drone imagery is accurately estimating forest
carbon stock within official carbon offsetting certification standards.
Additionally, our baseline CNN model outperforms state-of-the-art
satellite-based forest biomass and carbon stock estimates for this type of
small-scale, tropical agro-forestry sites. We present this dataset to encourage
machine learning research in this area to increase accountability and
transparency of monitoring, verification and reporting (MVR) in carbon
offsetting projects, as well as scaling global reforestation financing through
accurate remote sensing.

本文介绍了一个基准数据集 ReforesTree，使用 RGB 无人机图像中的个体树检测，深度学习模型准确地估计了厄瓜多尔六个农林碳补偿区域的森林碳储量，以鼓励该领域的机器学习研究，提高监测、验证和报告在碳补偿项目中的透明度和监督，并通过准确的遥感来推动全球森林再造项目。