arXiv:2606.27277v1 Announce Type: new Abstract: 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
The increasing availability of satellite data and advancements in AI, particularly world models, are converging to enable more sophisticated Earth observation forecasting capabilities.
This development allows for more accurate and probabilistic predictions of Earth surface dynamics, crucial for strategic planning in sectors like agriculture, disaster management, and resource allocation.
The ability to model uncertainty in Earth Observation (EO) forecasts shifts from deterministic predictions to probabilistic outcomes, offering a more nuanced understanding of future environmental conditions.
- · Satellite data providers
- · Climate scientists
- · Agricultural firms
- · Disaster relief organizations
- · Traditional deterministic forecasting models
- · Organizations reliant on infrequent or inaccurate earth observation data
Improved Earth observation forecasts lead to better resource management and early warning systems for environmental events.
Enhanced predictive capabilities may inform policy decisions related to climate change adaptation and mitigation efforts.
The integration of such models could lead to new financial products and insurance mechanisms based on probabilistic environmental risks.
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Read at arXiv cs.AI