Summary of Self-supervised Spatial-temporal Learner For Precipitation Nowcasting, by Haotian Li et al.
Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
by Haotian Li, Arno Siebes, Siamak Mehrkanoon
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to precipitation nowcasting is proposed in this paper, which leverages the benefits of self-supervised learning and integrates it with spatial-temporal learning. The model, SpaT-SparK, comprises a CNN-based encoder-decoder structure pretrained with a masked image modeling (MIM) task and a translation network that captures temporal relationships among past and future precipitation maps in downstream tasks. Experimental results on the NL-50 dataset demonstrate that SpaT-SparK outperforms existing baseline supervised models, such as SmaAt-UNet, providing more accurate nowcasting predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new model for predicting weather at a local level within a 6-hour time frame. The approach uses self-supervised learning to learn representations without needing labeled data, and combines it with spatial-temporal learning. The model is tested on the NL-50 dataset and shows better results than existing models. |
Keywords
» Artificial intelligence » Cnn » Encoder decoder » Self supervised » Supervised » Translation » Unet
