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Forecasting Earth Surface Change in Embedding Space — arXiv:2611.02211

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02211
License (as archived): CC0-1.0
Archived: 2026-11-03T10:28:06.305Z
SHA-256 of the archived text: a0fad89ea9ddb35994c8ffd203ccd940d21165bc459059343ad6151fdd4f9786
Stable URL of this page: https://metraipolis.com/documento/dc_a960dca875ef

How to cite

Forecasting Earth Surface Change in Embedding Space — arXiv:2611.02211 — arXiv (en), https://arxiv.org/abs/2611.02211 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a960dca875ef (Metraipolis archive dc_a960dca875ef, sha256 a0fad89ea9ddb35994c8ffd203ccd940d21165bc459059343ad6151fdd4f9786)

BibTeX

@misc{dca960dca875ef,
  title = {Forecasting Earth Surface Change in Embedding Space — arXiv:2611.02211},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02211},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a960dca875ef; sha256 a0fad89ea9ddb35994c8ffd203ccd940d21165bc459059343ad6151fdd4f9786},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02211. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its future and plan mitigating actions. Most existing studies that rely on a time series of satellite observations forecast the spectral values at each pixel for a future target date. However, clouds, shadows, and sensor noise often corrupt these observations, so that spectral forecasters waste much of their capacity on low-level features or noise rather than real change. With the advent of Earth foundation models, we argue that forecasting in embedding space is a feasible and more effective alternative. To this end, we design a token space that handles irregular histories from different sensors and foundation models and supports forecasts at any query date. Tokens from this space are inputs to our proposed forecaster, E$^2$Forecast, which is a transformer with two branches: a Gaussian branch that directly predicts a mean point forecast and per-dimension standard deviations, and a rectified-flow branch that generates forecast ensembles. On Sentinel-1 and Sentinel-2 benchmarks, forecasting embeddings instead of spectral values halves the cosine distance (from 0.20 to 0.10) and greatly reduces overconfidence, lowering the z-score standard deviation from 2.63 to 1.22. By comparing new observations with its forecasts, E$^2$Forecast can also detect anomalous changes without labels or retraining, raising F$_1$ from 41.58 to 48.70 on a global benchmark. This consistent superiority is validated on nine remote sensing foundation models. Beyond change detection, we believe our approach advances the field by permitting effective reuse of diverse pretrained task heads. Our code is publicly available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02211 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:50.159Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).