Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
This work addresses a domain-specific problem in climate modeling for researchers, but it is incremental as it builds on existing CNN-RF methods with a data augmentation strategy.
The paper tackled the challenge of fusing abundant satellite data with sparse ground measurements for PM2.5 prediction by augmenting training data with pseudo-labels generated via ordinary kriging, resulting in improved spatial correlation and reduced prediction error in a CNN-RF model.
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.