Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation

• URL: http://arxiv.org/abs/2401.08061v1
• Date: Tue, 16 Jan 2024 02:42:45 GMT
• Title: Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
• Authors: Lei Duan, Ziyang Jiang, David Carlson
• Abstract summary: We propose a strategy to augment the training dataset by introducing unlabeled satellite images paired with pseudo-labels generated through a spatial technique known as ordinary kriging. 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.
• Score: 0.9175121581660474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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