EO-WM：一种基于物理信息的地球观测预测世界模型
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
摘要
地球观测预测的目标是在变化的气象条件下，通过卫星数据来预测未来地球表面的动态变化。在本文中，我们将这一任务视为一个部分观测、受天气影响的世界建模问题——其中天气作为影响因子，而由于观测数据不足以及未观测到的地表状态，预测结果仍然具有不确定性。然而，现有的方法无法完全捕捉到这种情况：确定性模型会将不确定性简化为单一的未来预测结果；而基于扩散的方法则通常将天气变量视为无差异的影响因子。现有的评估标准也主要关注重建精度，而非预测结果是否能够正确反映变化的天气条件。我们提出了一种用于多光谱地球观测预测的视频扩散Transformer模型——EO-WM。该模型采用了基于物理原理的约束框架，通过气候基线、天气异常以及累积的物理压力信号来表示气象影响。具体来说，它通过不同的约束机制来区分正常状态和异常状态，并随时间累积异常影响，从而能够捕捉到持续的热力和干旱影响。为了评估预测结果对天气变化的响应能力，我们引入了两个评估标准：一个用于评估在极端天气下植被退化情况的极端夏季评估标准，另一个则是用于测试在变化天气条件下预测结果的准确性季节配对评估标准。实验表明，EO-WM能够将预测中的归一化植被指数下降幅度误差减少5.63%，同时提高预测结果的命中率7.80%。而在标准的像素级指标上，该模型仍然具有竞争力。这些评估标准和模型将在https://github.com/Luo-Z13/EO-WM上以开源形式提供。
English 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 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.