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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.08061 |
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| _version_ | 1866914642325929984 |
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| author | Duan, Lei Jiang, Ziyang Carlson, David |
| author_facet | Duan, Lei Jiang, Ziyang Carlson, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation Duan, Lei Jiang, Ziyang Carlson, David Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.08061 |