EarthAlign: Learning Language-Aligned Representations of Earth System Data
Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive sectors. Existing multimodal weather systems primarily target task-specific prediction, generation, or question answering, while general-purpose alignment between Earth system data and language remains underexplored due to the limited availability of diverse language supervision grounded in meteorological conditions. To fill this gap, we introduce EarthAlign, a large-scale dataset with broad geographic coverage spanning 2000-2024 that combines 10 expert-curated natural event and disaster data sources with climatology-derived normal-weather samp...
| Authors | Fadillah Adamsyah Maani, Numan Saeed, Jiaqi Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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