EO-WM is a diffusion transformer that adds physically separated baseline-anomaly and cumulative-stress conditioning to probabilistic EO forecasting and validates it on two new weather-response benchmarks, reporting 5.63% and 7.80% relative gains on NDVI decline metrics.
Rs-worldmodel: a unified model for remote sensing understanding and future sense forecasting
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
VegSim uses recurrent latent dynamics to enable both standard NDVI forecasting and user-controlled scenario simulation of vegetation from sparse satellite and weather inputs.
citing papers explorer
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EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
EO-WM is a diffusion transformer that adds physically separated baseline-anomaly and cumulative-stress conditioning to probabilistic EO forecasting and validates it on two new weather-response benchmarks, reporting 5.63% and 7.80% relative gains on NDVI decline metrics.
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VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation
VegSim uses recurrent latent dynamics to enable both standard NDVI forecasting and user-controlled scenario simulation of vegetation from sparse satellite and weather inputs.