EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this task as a partially observed, weather-driven world modeling problem, in which weather acts as a conditioning signal, while forecasting remains uncertain due to sparse observations and unobserved land-surface states. However, existing methods do not fully capture this setting: deterministic models collapse uncertainty into a single future prediction, while diffusion-based methods typically treat weather variables as undifferentiated conditioning signals, and existing benchmarks focus mainly on reconstruction accur...
| Authors | Junwei Luo, Shuai Yuan, Zhenya Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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