地球观测(EO)预测旨在根据不断变化的气象条件下的卫星观测来预测未来地球表面的动态。在本文,中，我们将此任务视为部分观测,天气驱动的世界建模问题,，其中天气充当调节信号,，而由于观测稀疏和未观测到的陆地表面状态，预测仍然不确定。然而,现有方法没有完全捕捉到这种设置:确定性模型将不确定性压缩为单个未来预测,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,并且现有基准主要关注重建精度而不是预测是否正确响应天气变化此http URL介绍了EO-WM,用于多光谱EO预测的视频扩散变压器。 EO-WM 结合了物理信息调节框架，通过气候基线, 天气异常, 和累积物理压力信号来表示气象强迫。具体来说，, 它通过不同的调节途径, 将基线和异常分开，并随着时间的推移积累异常强迫，以捕获持续的热和干旱胁迫。为了评估超出标准指标,的天气响应行为，我们引入了两个诊断基准:，一个是极端夏季基准，用于在极端天气,下对植被退化进行严重性预测，另一个是季节性匹配对基准，用于测试变化的天气强迫下的响应保真度。实验表明，EO-WM 将预测的标准化差异植被指数 (NDVI) 下降幅度的误差相对降低了 5.63%，并将定向命中率提高了相对 7.80%,，同时在标准像素级指标上保持竞争力。基准测试和模型将在此 https URL 开源。
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 this http URL 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 this https URL.
科目: 人工智能 (cs.AI); 计算机视觉和模式识别 (cs.CV)
Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)