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arXiv · 2511.10370

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation

Maria Gonzalez-Calabuig·Kai-Hendrik Cohrs·Vishal Nedungadi·Zuzanna Osika·Ruben Cartuyvels·Steffen Knoblauch·Joppe Massant·Shruti Nath·Patrick Ebel·Vasileios Sitokonstantinou
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Abstract

Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for reliability-aware prediction that enables GFMs to identify and abstain from likely failures. Our approach integrates three complementary signals: geophysical out-of-distribution (OOD) detection in the input space, OOD detection in the embedding space, and task-specific predictive uncertainty. We evaluate SHRUG-FM across three high-stakes rapid-mapping tasks: burn scar segmentation, flood mapping, and landslide detection. Our results show that SHRUG-FM consistently reduces prediction risk on retained samples, outperforming established single-signal baselines like predictive entropy. Crucially, by utilizing a shallow "glass-box" decision tree for signal fusion, SHRUG-FM provides interpretable abstention thresholds. It builds a pathway toward safer and more interpretable deployment of GFMs in climate-sensitive applications, bridging the gap between benchmark performance and real-world reliability.

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Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi, Zuzanna Osika, Ruben Cartuyvels, Steffen Knoblauch, Joppe Massant, Shruti Nath, Patrick Ebel, Vasileios Sitokonstantinou. 2025-11-13. SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation. https://arxiv.org/abs/2511.10370

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