File size: 1,906 Bytes
0b13e97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ---
task_categories:
- time-series-forecasting
tags:
- climate
- time-series
- satellite-imagery
license: unknown
---
# ClimateBench-M: A Multi-modal Climate Data Benchmark
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:
1. Time-series climate variables from ERA5.
2. Extreme weather event records from NOAA.
3. Satellite imagery from NASA HLS.
* [**ClimateBench-M-TS**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-TS): Climate time series with extreme events aligned and labeled.
* [**ClimateBench-M-IMG**](https://huggingface.co/datasets/Violet24K/ClimateBench-M-IMG): Satellite imagery data
* [**📖 Paper**](https://arxiv.org/abs/2404.00225)
If you find this repository useful in your research, please consider citing the following paper:
```
@inproceedings{DBLP:conf/kdd/ZhengJLTH24,
author = {Lecheng Zheng and
Baoyu Jing and
Zihao Li and
Hanghang Tong and
Jingrui He},
editor = {Ricardo Baeza{-}Yates and
Francesco Bonchi},
title = {Heterogeneous Contrastive Learning for Foundation Models and Beyond},
booktitle = {Proceedings of the 30th {ACM} {SIGKDD} Conference on Knowledge Discovery
and Data Mining, {KDD} 2024, Barcelona, Spain, August 25-29, 2024},
pages = {6666--6676},
publisher = {{ACM}},
year = {2024},
url = {https://doi.org/10.1145/3637528.3671454},
doi = {10.1145/3637528.3671454},
timestamp = {Sun, 08 Sep 2024 16:05:58 +0200},
biburl = {https://dblp.org/rec/conf/kdd/ZhengJLTH24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |