A Primer for Assessing Foundation Models for Earth Observation

Foundation Models (FMs) represent a transformative advancement for Earth Observation (EO), promising enhanced analysis and interpretation of complex spatial, spectral, and temporal data. However, selecting an appropriate FM for EO tasks remains challenging due to diverse model architectures, varied pretraining strategies, and nuanced evaluation metrics. Traditional benchmark-focused assessments are inadequate for capturing the full scope of EO-specific requirements, risking ineffective model selection. To address this, we introduce a structured assessment framework emphasizing four critical dimensions: pretraining data characteristics, tokenization processes, architectural choices, and downstream use case evaluations. This approach enables EO practitioners to systematically evaluate FMs based on data preprocessing rigor, token embedding strategies, model architecture suitability, and robustness in diverse real-world scenarios. Demonstrating the framework's practicality through comparative analysis of Prithvi-HLS models, our approach fosters informed, context-specific model integration, bridging the gap between FM developers and EO scientists and promoting responsible, effective FM utilization in EO workflows.