A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning
Abstract
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.
Cite
Text
Yaghmour et al. "A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.Markdown
[Yaghmour et al. "A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.](https://mlanthology.org/cvprw/2025/yaghmour2025cvprw-sensor/)BibTeX
@inproceedings{yaghmour2025cvprw-sensor,
title = {{A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning}},
author = {Yaghmour, Anan and Crawford, Melba M. and Prasad, Saurabh},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2025},
pages = {3047-3056},
url = {https://mlanthology.org/cvprw/2025/yaghmour2025cvprw-sensor/}
}