Now We Know? A Systematic Comparison of TerraMind and THOR
Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact? This study addresses that gap through a controlled comparison of two GFMs developed under European Space Agency's $Φ$-lab with contrasting design philosophies: THOR, which introduces a compute-adaptive architecture supporting variable patch sizes and unifies Sentinel-1, -2, and -3 data at their native resolutions; and TerraMind, a multimodal generative GFM pretrained with a dual-scale token/pixel objective that enables any-to-any cross-mod...
| Authors | Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 24 July 2026 |
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