[EO-WM](EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting)</p>\n","updatedAt":"2026-06-26T14:43:36.959Z","author":{"_id":"64a3c6800924dcbf938e7216","avatarUrl":"/avatars/4c0d7e9459e87ca53e620c7dfbbe3688.svg","fullname":"Junwei Luo","name":"ll-13","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":6,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6665343046188354},"editors":["ll-13"],"editorAvatarUrls":["/avatars/4c0d7e9459e87ca53e620c7dfbbe3688.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.27277","authors":[{"_id":"6a3e8ff40dbbc53604b66475","name":"Junwei Luo","hidden":false},{"_id":"6a3e8ff40dbbc53604b66476","name":"Shuai Yuan","hidden":false},{"_id":"6a3e8ff40dbbc53604b66477","name":"Zhenya Yang","hidden":false},{"_id":"6a3e8ff40dbbc53604b66478","name":"Yansheng Li","hidden":false},{"_id":"6a3e8ff40dbbc53604b66479","name":"Zhe Liu","hidden":false},{"_id":"6a3e8ff40dbbc53604b6647a","name":"Hengshuang Zhao","hidden":false}],"publishedAt":"2026-06-25T00:00:00.000Z","submittedOnDailyAt":"2026-06-26T00:00:00.000Z","title":"EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting","submittedOnDailyBy":{"_id":"64a3c6800924dcbf938e7216","avatarUrl":"/avatars/4c0d7e9459e87ca53e620c7dfbbe3688.svg","isPro":false,"fullname":"Junwei Luo","user":"ll-13","type":"user","name":"ll-13"},"summary":"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.","upvotes":2,"discussionId":"6a3e8ff40dbbc53604b6647b","githubRepo":"https://github.com/Luo-Z13/EO-WM","githubRepoAddedBy":"user","ai_summary":"EO-WM is a video diffusion transformer for multispectral Earth Observation forecasting that incorporates physically informed conditioning frameworks to better capture weather-driven uncertainties in land-surface dynamics.","ai_keywords":["video diffusion transformer","multispectral EO forecasting","physically informed conditioning framework","meteorological forcing","climatological baseline","weather anomalies","cumulative physical stress signals","diffusion models","Normalized Difference Vegetation Index","NDVI"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":4},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"64a3c6800924dcbf938e7216","avatarUrl":"/avatars/4c0d7e9459e87ca53e620c7dfbbe3688.svg","isPro":false,"fullname":"Junwei Luo","user":"ll-13","type":"user"},{"_id":"696da0962b3e2d9587d0b35d","avatarUrl":"/avatars/4f6c177ad51fb687ca1be75d18f6f5d6.svg","isPro":false,"fullname":"mini","user":"mini0999","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.27277.md","query":{}}">
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
Abstract
EO-WM is a video diffusion transformer for multispectral Earth Observation forecasting that incorporates physically informed conditioning frameworks to better capture weather-driven uncertainties in land-surface dynamics.
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.
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[EO-WM](EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting)
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Cite arxiv.org/abs/2606.27277 in a model README.md to link it from this page.
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