Operationalizing a Geospatial Foundation Model: Principles, Framework, Readiness Profile, Failure Modes, and Future Directions

Geospatial foundation models (GeoFMs) are entering operational Earth observation (EO) pipelines for disaster response, agricultural monitoring, and environmental accounting. Although published performance on canonical benchmarks has improved, the conditions under which a deployed pipeline behaves predictably at scale remain poorly characterized. We argue that the operational behavior of a deployed GeoFM is predominantly shaped by the system surrounding the model. The system components consist of preprocessing protocols, reference products, post-processing steps that assemble chunked inferences into final geospatial products, serving infrastructure, and governance interfaces, and only secondarily by the foundation model in isolation. Yet, no existing evaluation practice systematically examines this scaffold. This article addresses that gap through three methodological contributions and a released implementation. First, we propose a unique systems framework that decomposes a deployed GeoFM pipeline into three pillars (data, model, deployment) and a governance layer and identifies eight axes (P1-P8) along which operational readiness can be characterized. Second, we introduce an Operational Readiness profile that converts the framework into a measurable vector with both structural and magnitude components per axis, computable for any deployed pipeline. Third, we apply the profile to deployed pipelines built on the NASA-IBM Prithvi-EO 2.0 GeoFM across three downstream tasks: burn scar detection, flood inundation mapping, and crop classification. The evaluation spans 17 operational case events and approximately 250 controlled red-team inferences validated against independent reference products per task family. The measurements identify failure modes that recur at the interfaces between pillars and are usually not surfaced by model-level evaluation. Two findings illustrate the stakes. First, the conventional HLS quality mask removes up to 17% of the burn target itself, misclassified as water, cutting per-event recall by up to 0.27 with no signal to the operator, while the identical layer serves as a validation reference in the flood pipeline. Second, evaluating the flood predictions against optical versus radar references yields statistically distinct cloud sensitivities (non-overlapping 95% confidence intervals). While Prithvi-EO 2.0 serves as our testbed, the profile itself is model-agnostic and extends to any deployed GeoFM pipeline, providing a transferable basis for assessing operational trustworthiness as these models enter production use.