Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label
  Generation
Lei Duan Ziyang Jiang David Carlson
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.