Controlled Comparison of Geospatial Foundation Models TerraMind and THOR Reveals Architecture Matters More Than Model Identity
A systematic comparison of two geospatial foundation models, TerraMind and THOR, finds that architectural choices—particularly patch size and decoder type—account for more performance variance than the specific model identity. The study, conducted under the European Space Agency's Φ-lab, also highlights that TerraMind and THOR represent complementary strategies: TerraMind emphasizes pretraining-time scale, while THOR focuses on inference-time tokenization. The authors propose a diagnostic ablation methodology for understanding model differences across diverse geospatial tasks.
Why it matters: This research offers a new methodology for diagnosing and interpreting performance differences in geospatial AI models, moving beyond aggregate leaderboards to inform future model development.
Full story at: arXiv Machine Learning ↗