Assessing the Robustness of Prithvi Geospatial Foundation Model for Coastal Habitat Mapping Under Data Availability and Domain Shift Scenarios

Authors

• Sam Khallaghi, Clark University
• Rishi Singh, Clark University
• Rahebeh Abedi, Clark University
• Ronald J. Eastman, Clark University
• Lyndon Estes, Clark UniversityFollow
• Sujit Roy, University of Alabama, Huntsville
• Rahul Ramachandran, NASA Marshall Space Flight Center
• Hamed Alemohammad, Clark UniversityFollow

Document Type

Article

Abstract

Self-supervised learning techniques and geospatial foundation models are garnering increasing attention within the remote sensing community, marking a pivotal shift toward more adaptable, generalizable and data efficient methodologies. Despite their growing popularity, comparative analyses between these emergent models and traditional, task-specific supervised deep learning models for tasks such as semantic segmentation remain largely unexplored. This study is formulated as a comparative analysis to scrutinize the efficacy of the finetuned Prithvi geospatial foundation models against a supervised convolutional neural network trained from scratch for semantic segmentation of mangrove forests and pond aquaculture under varying levels of data availability and domain shift. Mangrove ecosystems are vital for coastal protection, biodiversity, and climate change mitigation. However, their conversion for economic activities like pond aquaculture requires timely and accurate land use land cover maps for effective monitoring. We introduce a novel labeled dataset tailored for this purpose and divide the training data into subsets (100%, 50%, 25%, 12.5%, and 5% sample size) to evaluate model performance under varying degrees of data availability. Our findings reveal that the U-Net model exhibits superior performance on all experiments except the 12.5% and 5% subsets. However, considering the difference between the pretraining and finetuning strategies, the results highlight the potential of foundation models to overcome the constraints of annotated data availability in remote sensing applications, offering a promising avenue for the scalable and efficient monitoring of critical coastal ecosystems.

Publication Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Publication Date

2026

Volume

19

First Page

18840

Last Page

18870

ISSN

1939-1404

DOI

10.1109/JSTARS.2026.3698337

Keywords

Coastal habitats, geospatial foundation model, Prithvi, self-supervised learning, semantic segmentation, supervised learning, U-net

Repository Citation

Khallaghi, Sam; Singh, Rishi; Abedi, Rahebeh; Eastman, Ronald J.; Estes, Lyndon; Roy, Sujit; Ramachandran, Rahul; and Alemohammad, Hamed, "Assessing the Robustness of Prithvi Geospatial Foundation Model for Coastal Habitat Mapping Under Data Availability and Domain Shift Scenarios" (2026). Geography. 1056.
https://commons.clarku.edu/faculty_geography/1056

Cross Post Location

Student Publications

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Conditions

Khallaghi, S., Singh, R., Abedi, R., Eastman, J. R., Estes, L. D., Roy, S., ... & Alemohammad, H. (2026). Assessing the Robustness of Prithvi Geospatial Foundation Model for Coastal Habitat Mapping under Data Availability and Domain Shift Scenarios. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.