EO-WM：一种物理信息引导的概率性地球观测预测世界模型

摘要

地球观测（EO）预测旨在根据卫星观测在变化的气象条件下预测未来的地球表面动态。本文将这一任务视为一个部分观测的、天气驱动的世界建模问题，其中天气作为条件信号，但由于观测稀疏和未观测的地表状态，预测仍存在不确定性。然而，现有方法并未完全捕捉这一设定：确定性模型将不确定性压缩为单一未来预测，而基于扩散的方法通常将天气变量视为无差别的条件信号，现有基准主要关注重建精度，而非预测是否对变化的天气强迫做出正确响应。我们提出EO-WM，一种用于多光谱地球观测预测的视频扩散Transformer。EO-WM采用物理信息条件框架，通过气候基线、天气异常和累积物理胁迫信号来表示气象强迫。具体而言，它通过不同的条件路径分离基线与异常，并随时间累积异常强迫以捕捉持续的热胁迫和干旱胁迫。为了在标准指标之外评估天气响应行为，我们引入两个诊断基准：极端夏季基准（用于极端天气下植被退化的严重程度感知预测）和季节配对基准（用于测试在变化的天气强迫下的响应保真度）。实验表明，EO-WM在预测归一化植被指数（NDVI）下降幅度上的误差相对降低5.63%，方向命中率相对提升7.80%，同时在标准像素级指标上保持竞争力。该基准和模型将在https://github.com/Luo-Z13/EO-WM开源。

English

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 accuracy rather than whether forecasts respond correctly to changed weather forcing.We introduce EO-WM, a video diffusion transformer for multispectral EO forecasting. EO-WM incorporates a physically informed conditioning framework that represents meteorological forcing through a climatological baseline, weather anomalies, and cumulative physical stress signals. Specifically, it separates baseline and anomaly through distinct conditioning pathways, and accumulates anomalous forcing over time to capture sustained heat and drought stress. To evaluate weather-response behavior beyond standard metrics, we introduce two diagnostic benchmarks: an Extreme Summer Benchmark for severity-aware prediction of vegetation degradation under extreme weather, and a Seasonal Matched-Pair Benchmark for testing response fidelity under changed weather forcing. Experiments show that EO-WM reduces the error in predicted Normalized Difference Vegetation Index (NDVI) decline amplitude by a relative 5.63% and improves directional hit rate by a relative 7.80%, while remaining competitive on standard pixel-level metrics. The benchmarks and model will be made open-source at https://github.com/Luo-Z13/EO-WM.