非洲农田制图的弱标注方法
Weak Labeling for Cropland Mapping in Africa
AI总结：
本文提出利用无监督对象聚类细化全球农田地图等弱标签，并结合稀疏人工标注训练语义分割网络，在仅33个人工标注下将非洲农田F_1分数从0.53提升至0.84。
摘要：
农田制图在应对环境、农业和粮食安全挑战方面可以发挥至关重要的作用。然而，在非洲背景下，实际应用往往受到高分辨率农田地图可用性有限的制约。此类地图通常需要大量人工标注，从而形成可扩展性瓶颈。为解决这一问题，我们提出一种利用无监督对象聚类来细化现有弱标签的方法，例如从全球农田地图中获得的弱标签。细化后的标签与稀疏人工标注一起，作为语义分割网络的训练数据，该网络旨在识别农田区域。我们进行实验以证明由我们的方法生成的改进弱标签所带来的益处。在仅使用33个人工标注标签训练模型的场景中，当加入挖掘出的负标签后，农田类别的F_1分数从0.53提升至0.84。
英文摘要：
Cropland mapping can play a vital role in addressing environmental, agricultural, and food security challenges. However, in the context of Africa, practical applications are often hindered by the limited availability of high-resolution cropland maps. Such maps typically require extensive human labeling, thereby creating a scalability bottleneck. To address this, we propose an approach that utilizes unsupervised object clustering to refine existing weak labels, such as those obtained from global cropland maps. The refined labels, in conjunction with sparse human annotations, serve as training data for a semantic segmentation network designed to identify cropland areas. We conduct experiments to demonstrate the benefits of the improved weak labels generated by our method. In a scenario where we train our model with only 33 human-annotated labels, the F_1 score for the cropland category increases from 0.53 to 0.84 when we add the mined negative labels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
