The recent explosion of Earth Observation Foundation Models (EO-FMs), such as AlphaEarth, TerraMind, TESSERA, and Copernicus-FM, has introduced unprecedented capabilities in GeoAI. However, their adoption presents considerable challenges, including high computational demands, diverse multimodal data requirements, and substantial obstacles to model interpretation.

To address this "model fatigue," this talk offers a practical guide to facilitate transparent and efficient model selection for data practitioners. We will survey the current landscape of GeoAI models, demonstrate methods for evaluating predictive performance using accessible open-source tools, and present alternative architectures that enable scalable (parallel) processing on standard cloud GPUs, eliminating the need for high-performance computing clusters to test model accuracy on smaller data domains and feature combinations. FMs are often presented as the single truth, but findings from combining feature sets from multiple EO-FMs at the prediction level suggest that ensemble approaches outperform any single model.

Finally, we will address essential semantic and temporal considerations to ensure attendees understand the hidden uncertainties in their geospatial embeddings before making operational decisions, and why the responsible use of AI-derived data requires that automated outputs never be treated as ground truth. Foundation Models are designed for specific purposes, and despite being termed "Foundation Models," there is no single model that fits all use cases — project domains are often highly specific, and closing that gap may require targeted model modification to generate custom embeddings that address what standard models miss.