通过基于克里金的伪标签生成增强地面PM2.5预测
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
AI总结：
本文提出利用普通克里金法为未标注卫星图像生成伪标签以扩充训练数据，从而提升CNN-RF模型对地面PM2.5的预测性能，改善空间相关性并降低预测误差。
摘要：
融合丰富的卫星数据与稀疏的地面测量数据是气候建模中的一大挑战。为解决这一问题，我们提出了一种策略，通过引入未标注的卫星图像，并配以通过一种称为普通克里金（ordinary kriging）的空间插值技术生成的伪标签，来扩充训练数据集，从而充分利用可用的卫星数据资源。我们表明，所提出的数据增强策略有助于以合理的幅度提升最先进的卷积神经网络-随机森林（CNN-RF）模型的性能，从而在空间相关性方面带来显著改善，并降低预测误差。
英文摘要：
Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dataset by introducing unlabeled satellite images paired with pseudo-labels generated through a spatial interpolation technique known as ordinary kriging, thereby making full use of the available satellite data resources. We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.
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。
