论文标题
Forestnet：使用卫星图像上深度学习对印度尼西亚森林砍伐的驱动力进行分类
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
论文作者
论文摘要
表征导致森林砍伐的过程对于有针对性的森林保护和管理政策的发展和实施至关重要。在这项工作中，我们开发了一种名为Forestnet的深度学习模型，以对印度尼西亚原发性森林损失的驱动力进行分类，这是世界上森林砍伐率最高的国家之一。使用卫星图像，ForestNet确定了任何大小的森林损失斑块中森林砍伐的直接驱动因素。我们策划了Landsat的数据集8已知森林损失事件的卫星图像，并配对专家口译员的驾驶员注释。我们使用数据集来训练和验证模型，并证明ForestNet显然优于其他标准驱动程序分类方法。为了支持对自动化砍伐驱动器分类方法的未来研究，本研究中策划的数据集可在https://stanfordmlgroup.github.github.io/projects/forestnet上公开获得。
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at https://stanfordmlgroup.github.io/projects/forestnet .
