PDF(Pubmed)
Abstract：
Understanding direct deforestation drivers at a fine spatial and temporal scale is needed to design appropriate measures for forest management and monitoring. To achieve this, reference datasets with which to design Artificial Intelligence (AI) approaches to classify direct deforestation drivers within areas experiencing forest loss in a detailed, comprehensive and locally-adapted way are needed. This is the case for Cameroon, in the Congo Basin, which has known increasing deforestation rates in recent years. Here, we created an Earth Observation dataset with associated labels to classify detailed direct deforestation drivers in Cameroon, which includes satellite imagery (Landsat and PlanetScope) and auxiliary data on infrastructure and biophysical properties. The dataset provides the following fifteen labels: oil palm, timber, fruit, rubber and other-large scale plantations; grassland/shrubland; small-scale oil palm or maize plantations and other small-scale agriculture; mining; selective logging; infrastructure; wildfires; hunting; and other.
摘要：
需要在精细的空间和时间尺度上了解直接的森林砍伐驱动因素，以设计适当的森林管理和监测措施。为了实现这一点，参考数据集，用于设计人工智能(AI)方法，对经历森林损失的地区内的直接森林砍伐驱动因素进行详细分类，需要全面和适应当地的方式。喀麦隆就是这样,在刚果盆地，近年来森林砍伐率不断上升。这里,我们创建了一个带有相关标签的地球观测数据集，以对喀麦隆的详细直接森林砍伐驱动因素进行分类，其中包括卫星图像（Landsat和PlanetScope）以及有关基础设施和生物物理特性的辅助数据。该数据集提供了以下十五个标签：油棕，木材,水果,橡胶和其他大型种植园；草地/灌木丛；小规模油棕或玉米种植园和其他小规模农业；采矿；选择性伐木；基础设施；野火；狩猎；和其他。
