# Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding Source: [Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding]() Collection index: [llms.txt]() Data updated: 2026-03-11T05:00:04.181Z Authors: Lance Legel, Qin Huang, Brandon Voelker, Daniel Neamati, Patrick Alan Johnson, Favyen Bastani, Jeff Rose, James Ryan Hennessy, Robert Guralnick, Douglas Soltis, Pamela Soltis, Shaowen Wang Published: 2026-03-07T05:13:20.000Z ## Abstract We present DeepEarth, a self-supervised multi-modal world model with Earth4D, a novel planetary-scale 4D space-time positional encoder. Earth4D extends 3D multi-resolution hash encoding to include time, efficiently scaling across the planet over centuries with sub-meter, sub-second precision. Multi-modal encoders (e.g. vision-language models) are fused with Earth4D embeddings and trained via masked reconstruction. We demonstrate Earth4D's expressive power by achieving state-of-the-art performance on an ecological forecasting benchmark. Earth4D with learnable hash probing surpasses a multi-modal foundation model pre-trained on substantially more data. Access open source code and download models at: https://github.com/legel/deepearth ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Live Fuel Moisture Content (LFMC) Prediction on Globe-LFMC 2.0 (test)](): Live Fuel Moisture Content (LFMC) Prediction; Globe-LFMC 2.0 (test); MAE: 11.7. ## Original sources and implementation [Original paper]() [PDF]()