EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

  • 类型：arxiv
  • 标识：2606.27277
  • 链接：https://arxiv.org/abs/2606.27277
  • 主分类：multimodal
  • 形态：method
  • 被引：1
  • 被引来源：Semantic Scholar
  • S2被引：1
  • OpenAlex被引：0
  • 影响力被引：0
  • TLDR：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 un
  • OpenAlex ID：W7166111328
  • OpenAlex DOI：10.48550/arxiv.2606.27277
  • DOI：10.48550/arxiv.2606.27277
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://doi.org/10.48550/arxiv.2606.27277
  • OpenAlex更新：2026-07-19
  • 待LLM分类：否
  • 标题中文：EO-WM：面向概率性地球观测预报的物理信息世界模型
  • TLDR中文：地球观测（EO）预报旨在依据变化的天气条件，从卫星观测预测未来地表动态。本文将其建模为部分可观测、天气驱动的世界建模问题，其中天气作为条件信号，而由于观测稀疏和未观测的陆面状态，预报本身具有不确定性。然而现有方法未能完整刻画这一设定：确定性模型将不确定性坍缩为单一未来预测，而基于扩散的方法通常将天气变量视作无条（原文此句截断）。
  • 来源文件：
  • /inbox/tom/_candidates/2026-06-29-agent-memory-tool-use-candidates.json
  • [S2 enrich]
  • [OpenAlex backfill]