Online Earth monitoring with multi-modal Satellite Image Time Series

While online Earth monitoring with Satellite Image Time Series (SITS) is essential for tracking rapid anthropogenic changes, existing remote sensing foundation models remain unsuited for streaming SITS. We introduce IRMA, the first foundation model tailored for online land monitoring. IRMA leverages a novel dual-form framework that unifies parallelized multi-modal pre-training (Sentinel-1/2) with an efficient recurrent inference mechanism. Our self-supervised objective produces latent representations that simultaneously maintain temporal stability against seasonal variations and sensitivity to permanent land modifications. To evaluate our method, we present MELBA, a multi-temporal benchmark spanning land-cover, building density, and gold-panning tasks. Experimental results show that IRMA achieves competitive performance with state-of-the-art baselines in the mono-modal setting while using fewer parameters. Furthermore, qualitative analyses suggest that IRMA effectively fuses multi-modal observations, thus providing representations relevant for online monitoring.

@article{dumeur2026irma,
  title={},
  author={Dumeur, Iris and Aitola, Aitor and Anger, J{\'e}r{\'e}my and Facciolo, Gabriele},
  journal={arXiv preprint},
  year={2026}
}