| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 100B<n<1T |
| tags: |
| - climate |
| - synthetic |
| task_categories: |
| - zero-shot-image-classification |
| --- |
| |
| <div align="center"> |
|
|
| # EarthAlign: Learning Language-Aligned Representations of Earth System Data |
|
|
|  |
|
|
| **Fadillah Adamsyah Maani<sup>1</sup>, Numan Saeed<sup>1</sup>, Jiaqi Ma<sup>1</sup>, Dat Tien Nguyen<sup>1</sup>, Youssef Wehbe<sup>2</sup>, Duncan Watson-Parris<sup>3</sup>, Salman Khan<sup>1,2</sup>** |
|
|
| <sup>1</sup>Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), <sup>2</sup>ADIA Lab, <sup>3</sup>University of California, San Diego (UC San Diego) |
|
|
| [](https://mbzuai-oryx.github.io/EarthAlign/) |
| [](https://arxiv.org/abs/2610.14624) |
| [](https://github.com/mbzuai-oryx/EarthAlign) |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🌍 Overview |
|
|
| **EarthAlign** is a large-scale Earth system-language alignment dataset and benchmark for learning and evaluating multimodal representations of Earth system data. It contains **553,738 samples** spanning **2000-2024**, with broad geographic coverage and diverse natural phenomena and normal weather conditions. Each sample pairs temporal and geospatial information and multivariate ERA5 data with four metadata-grounded caption styles (natural-event-focused, impact-focused, physical framing, and atmospheric pattern) that describe the same Earth system condition from different perspectives. |
|
|
| Building on EarthAlign, **MeteoLIP** is a CLIP/SigLIP-based dual-encoder framework for aligning meteorological data with natural language. Its meteorology tokenizer, **MetTok**, aggregates variable-specific representations into compact meteorology tokens while incorporating temporal and geospatial information. MeteoLIP is evaluated on cross-modal retrieval, weather-condition classification, and physical variable estimation. |
|
|
| **This Hugging Face repository hosts the EarthAlign dataset and the pretrained MeteoLIP weights.** The code for preprocessing, training, evaluation, and linear probing is available in the [GitHub repository](https://github.com/mbzuai-oryx/EarthAlign). |
|
|
| --- |
|
|
| ## 📦 Repository Contents |
|
|
| ``` |
| MBZUAI/EarthAlign |
| ├── EarthAlign/ # EarthAlign dataset (~241 GB) |
| │ ├── webdatasets_train/ # Training data in WebDataset format (.tar shards, ~212 GB) |
| │ ├── data_era5_processed/ # Test/evaluation data (~27.6 GB, tar archives to extract) |
| │ │ ├── npz/ # Preprocessed ERA5 samples (.npz) |
| │ │ ├── json/ # Multi-style captions (.json) |
| │ │ └── info.json |
| │ └── data/ |
| │ └── record_json/ # Full dataset records: per-source natural event and |
| │ # weather-normal (negsamp) samples with captions and metadata |
| └── MeteoLIP/ # Pretrained MeteoLIP weights (~17.2 GB) |
| ├── Meteo-CLIP/ |
| │ ├── ViT-B-4_Full-MetTok/ |
| │ └── ViT-L-4_Full-MetTok/ |
| └── Meteo-SigLIP/ |
| ├── ViT-B-4_Full-MetTok/ |
| └── ViT-L-4_Full-MetTok/ |
| ``` |
|
|
| ### MeteoLIP Weights |
|
|
| | Model | Meteorology Encoder | MetTok Configuration | Weights | |
| | ------------------ | ------------------- | -------------------- | ------- | |
| | Meteo-CLIP ViT-B | ViT-B/4 | Full MetTok | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-CLIP/ViT-B-4_Full-MetTok) | |
| | Meteo-CLIP ViT-L | ViT-L/4 | Full MetTok | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-CLIP/ViT-L-4_Full-MetTok) | |
| | Meteo-SigLIP ViT-B | ViT-B/4 | Full MetTok | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-SigLIP/ViT-B-4_Full-MetTok) | |
| | Meteo-SigLIP ViT-L | ViT-L/4 | Full MetTok | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-SigLIP/ViT-L-4_Full-MetTok) | |
|
|
| --- |
|
|
| ## ⚖️ License |
|
|
| This repository is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). EarthAlign is built on ERA5 reanalysis data and natural event records from multiple public sources, each with its own license or terms of use. Please refer to the [natural event record sources](https://github.com/mbzuai-oryx/EarthAlign#natural-event-record-sources) for details. |
|
|
| --- |
|
|
| ## 🙏 Acknowledgement |
|
|
| This material is based on work supported by ADIA Lab, Abu Dhabi under a collaborative research project titled Climate Co-Pilot: A Multimodal AI Agent for Climate Resilience in the MENA Region. |
|
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| Our codebase is built upon the [OpenCLIP](https://github.com/mlfoundations/open_clip) repository, and we thank sincerely its developers and contributors for making their work publicly available. |
|
|
| --- |
|
|
| ## 📜 Citation |
|
|
| If you find EarthAlign or MeteoLIP useful in your research, please consider citing: |
|
|
| ```bibtex |
| @misc{maani2026earthalignlearninglanguagealignedrepresentations, |
| title={EarthAlign: Learning Language-Aligned Representations of Earth System Data}, |
| author={Fadillah Adamsyah Maani and Numan Saeed and Jiaqi Ma and Dat Tien Nguyen and Youssef Wehbe and Duncan Watson-Parris and Salman Khan}, |
| year={2026}, |
| eprint={2610.14624}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2610.14624}, |
| } |
| ``` |
