Guard-railing Geospatial Foundation Models

Geospatial Foundation Models (GFMs) have emerged as powerful tools for downstream Earth Observation (EO) tasks like flood detection, deforestation and urbanization. However, their practical application can pose avoidable risks, when operationalizing such models at scale. We propose a novel approach to guard-railing application-specific geospatial AI models that combines data validation with both static and dynamic guard-rails to ensure more trustworthy geospatial inference results. For data validation, statistical consistency checks on model input and output is used to identify outliers, format-related input errors and specious predictions. In static guard-railing, techniques such as cloud masking, ocean masking and Land Use/Land Cover (LULC) priors are leveraged to constrain model outputs. Such static guard-rails can be both generic (applied to all models) and model-specific, applied in combination to best assure reliable results. For dynamic guard-railing, cross-model contrastive analysis is performed with so-called, sentry GFMs (e.g. IBM’s any-to-any TerraMind generative model), which serve as trusted, high-confidence LULC classifiers. Through this three-component guard-rail framework, we are able to constrain and flag predictions that violate topological expectations. For example, if a field segmentation model detects cropland within water bodies or atop buildings (infrastructure zones), such outputs are automatically rejected, masked, corrected or flagged for human inspection--based on the target GFM's predictive confidence or uncertainty. This guardrail service is available as a plug-and-play module for a suite of fine-tuned GFMs (e.g., Prithvi-EO 1.0/2.0, SatMAE) with minimal modifications on IBM’s Geospatial Studio platform.