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

arXiv: https://arxiv.org/abs/2606.27277 · HTML: https://mlsanity.com/paper/2606.27277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 21 of 21)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [Open-Sora 2.0: Training a Commercial-Level Video Generation Model in 200k](https://mlsanity.com/paper/2503.09642/) (2025)
- [Advancing Open-source World Models](https://mlsanity.com/paper/2601.20540/) (2026)
- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [Vid2World: Crafting Video Diffusion Models to Interactive World Models](https://mlsanity.com/paper/2505.14357/) (2025)
- [WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models](https://mlsanity.com/paper/2602.08971/) (2026)
- [ATI: Any Trajectory Instruction for Controllable Video Generation](https://mlsanity.com/paper/2505.22944/) (2025)
- [AVID: Adapting Video Diffusion Models to World Models](https://mlsanity.com/paper/2410.12822/) (2024)
- [WorldMark: A Unified Benchmark Suite for Interactive Video World Models](https://mlsanity.com/paper/2604.21686/) (2026)
- [GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation](https://mlsanity.com/paper/2512.12751/) (2025)
- [DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations](https://mlsanity.com/paper/2404.06517/) (2024)
- [Remote Sensing-Oriented World Model](https://mlsanity.com/paper/2509.17808/) (2025)
- [UniTS: Unified Spatio-Temporal Generative Model for Remote Sensing](https://mlsanity.com/paper/2512.04461/) (2025)
- [EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data](https://mlsanity.com/paper/2602.12177/) (2026)
- [RS-WorldModel: a Unified Model for Remote Sensing Understanding and Future Sense Forecasting](https://mlsanity.com/paper/2603.14941/) (2026)
- [Global Vegetation Modeling with Pre-Trained Weather Transformers](https://mlsanity.com/paper/2403.18438/) (2024)

## Built on by (most cited first, 1 of 1)

- [From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring](https://mlsanity.com/paper/2607.13651/) (2026)

## Abstract (arXiv)

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.