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U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping

Authors Vit Kostejn , Yamil Essus , Jenna Abrahamson , Ranga Raju Vatsavai

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Image segmentation
Keywords
  • GeoAI
  • flood mapping
  • foundation model
  • U-Net
  • Prithvi

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Abstract

In recent years, large pre-trained models, commonly referred to as foundation models, have become increasingly popular for various tasks leveraging transfer learning. This trend has expanded to remote sensing, where transformer-based foundation models such as Prithvi, msGFM, and SatSwinMAE have been utilized for a range of applications. While these transformer-based models, particularly the Prithvi model, exhibit strong generalization capabilities, they have limitations on capturing fine-grained details compared to convolutional neural network architectures like U-Net in segmentation tasks. In this paper, we propose a novel architecture, U-Prithvi, which combines the strengths of the Prithvi transformer with those of U-Net. We introduce a RandomHalfMaskLayer to ensure balanced learning from both models during training. Our approach is evaluated on the Sen1Floods11 dataset for flood inundation mapping, and experimental results demonstrate better performance of U-Prithvi over both individual models, achieving improved performance on out-of-sample data. While this principle is illustrated using the Prithvi model, it is easily adaptable to other foundation models.

Cite As Get BibTex

Vit Kostejn, Yamil Essus, Jenna Abrahamson, and Ranga Raju Vatsavai. U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 18:1-18:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GIScience.2025.18

Author Details

Vit Kostejn
  • Charles University, Prague, Czech Republic
Yamil Essus
  • Industrial and Systems Engineering Department, North Carolina State University, Raleigh, NC, USA
Jenna Abrahamson
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
Ranga Raju Vatsavai
  • Computer Science Department, North Carolina State University, Raleigh, NC, USA

Funding

  • Abrahamson, Jenna: Funded by a National Science Foundation Graduate Research Fellowship Grant No. DGE-2137100.

Supplementary Materials

References

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