# EO-WM：物理信息驱动的概率地球观测预测世界模型

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-25 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.virxact.com/items/cmqwqyy1i006hsliksaww74ga
- 原文链接：https://arxiv.org/abs/2606.27277

## AI 摘要

EO-WM是一种基于视频扩散Transformer的多光谱地球观测预测模型，将天气作为条件信号，稀疏观测与未观测地表状态视为不确定性来源。模型通过物理信息条件框架区分气候基线、天气异常和累积物理应力信号（如持续高温与干旱胁迫），并引入极端夏季基准和季节性匹配对基准，分别评估极端天气下植被退化预测的严重性感知能力及天气强迫变化下的响应保真度。实验表明，EO-WM在NDVI下降幅度预测上相对误差降低5.63%，方向命中率相对提升7.80%，同时保持标准像素级指标竞争力。模型与基准将开源。

## 正文

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.