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 (Hong Kong University of Science and Technology)
- 2. Yanyan Shen (Shanghai Jiao Tong University)
- 3. Lei Chen (Hong Kong University of Science and Technology)
- 4. Charles Wang Wai Ng (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{li_sigmod23,
title = {{SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation}},
author = {Li, Jia and Shen, Yanyan and Chen, Lei and Ng, Charles Wang Wai},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589321},
url = {https://dl.acm.org/doi/10.1145/3589321},
year = {2023}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 10,521 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 5.093636e-05 |
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| 1,337 | DB-BERT: A Database Tuning Tool that "Reads the Manual" | 2022 | SIGMOD | 0.00011117488 |
| 4,671 | PreQR: Pre-training Representation for SQL Understanding | 2022 | SIGMOD | 6.5732787e-05 |
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