Emerging Flexible Designs for Geospatial Multimodal Foundation Models

This study presents a comparative analysis of various foundation model architectures for geospatial multimodal reasoning, focusing on flexibility across different spectral band configurations. Using standardized self-supervised learning objectives and identical training datasets, the models were evaluated on the GEOBench benchmark for classification and segmentation tasks. The findings provide insights into the design trade-offs of model flexibility, modality alignment, and performance, offering practical guidance for developing advanced geospatial foundation models.