A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning

Remote sensing enables a wide range of critical applications such as land cover, land use mapping, crop yield prediction, and environmental monitoring. Advances in satellite technology have expanded RS datasets, yet high-performance segmentation models remain dependent on extensive labeled data-challenged by annotation scarcity and variability across sensors, illumination, and geography. Domain adaptation offers a promising solution to improve model generalization. This paper introduces a domain generalization approach to leveraging emerging geospatial foundation models by combining soft-alignment pseudo-labeling with source-to-target generative pre-training. We further provide new mathematical insights of MAE-based generative learning for domain-invariant feature learning. Experiments with hyperspectral and multispectral remote sensing datasets confirm our method’s effectiveness in enhancing adaptability and segmentation performance.