SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908979223855104 |
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| author | Gonzalez-Calabuig, Maria Cohrs, Kai-Hendrik Nedungadi, Vishal Osika, Zuzanna Cartuyvels, Ruben Knoblauch, Steffen Massant, Joppe Nath, Shruti Ebel, Patrick Sitokonstantinou, Vasileios |
| author_facet | Gonzalez-Calabuig, Maria Cohrs, Kai-Hendrik Nedungadi, Vishal Osika, Zuzanna Cartuyvels, Ruben Knoblauch, Steffen Massant, Joppe Nath, Shruti Ebel, Patrick Sitokonstantinou, Vasileios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation Gonzalez-Calabuig, Maria Cohrs, Kai-Hendrik Nedungadi, Vishal Osika, Zuzanna Cartuyvels, Ruben Knoblauch, Steffen Massant, Joppe Nath, Shruti Ebel, Patrick Sitokonstantinou, Vasileios Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.10370 |