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--- |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- climate |
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- time-series |
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- satellite-imagery |
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license: unknown |
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--- |
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# ClimateBench-M: A Multi-modal Climate Data Benchmark |
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ClimateBench-M is a multi-modal climate benchmark designed to support the development of artificial general intelligence (AGI) in climate applications. It aligns data across three critical modalities at a unified spatio-temporal resolution: |
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1. Time-series climate variables from ERA5. |
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2. Extreme weather event records from NOAA. |
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3. Satellite imagery from NASA HLS. |
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* [**ClimateBench-M-TS**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-TS): Climate time series with extreme events aligned and labeled. |
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* [**ClimateBench-M-IMG**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-IMG): Satellite imagery data |
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* [**📖 Paper**](https://arxiv.org/abs/2404.00225) |
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If you find this repository useful in your research, please consider citing the following paper: |
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``` |
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@inproceedings{DBLP:conf/kdd/ZhengJLTH24, |
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author = {Lecheng Zheng and |
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Baoyu Jing and |
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Zihao Li and |
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Hanghang Tong and |
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Jingrui He}, |
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editor = {Ricardo Baeza{-}Yates and |
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Francesco Bonchi}, |
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title = {Heterogeneous Contrastive Learning for Foundation Models and Beyond}, |
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booktitle = {Proceedings of the 30th {ACM} {SIGKDD} Conference on Knowledge Discovery |
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and Data Mining, {KDD} 2024, Barcelona, Spain, August 25-29, 2024}, |
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pages = {6666--6676}, |
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publisher = {{ACM}}, |
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year = {2024}, |
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url = {https://doi.org/10.1145/3637528.3671454}, |
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doi = {10.1145/3637528.3671454}, |
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timestamp = {Sun, 08 Sep 2024 16:05:58 +0200}, |
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biburl = {https://dblp.org/rec/conf/kdd/ZhengJLTH24.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
