SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Summary: SSIN is a self-supervised rainfall spatial interpolation framework built on SpaFormer, a Transformer that learns latent spatial patterns from historical data. Cloze-like masking provides self-supervision to capture spatial correlations, yielding state-of-the-art results on rainfall and traffic interpolation benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jia Li
- 2. Yanyan Shen
- 3. Lei Chen
- 4. Charles Wang Wai Ng
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 10,233 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 4.1905499e-05 |
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| 1,404 | DB-BERT: A Database Tuning Tool that "Reads the Manual" | 2022 | SIGMOD | 0.00012179714 |
| 4,658 | PreQR: Pre-training Representation for SQL Understanding | 2022 | SIGMOD | 6.0084453e-05 |
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