{
  "abstract": "Pretrained Earth observation models may reduce the effort needed to map surface water, but their value depends on the alternative against which they are compared. We evaluated Prithvi-EO-2.0-Tiny using the 446 hand-labeled Sentinel-2 chips from 11 flood events in Sen1Floods11. A fine-tuned model, a frozen-encoder model, and an identical randomly initialized architecture were compared with a similarly sized U-Net, a larger Prithvi model, and spectral-index thresholds. Experiments varied the number of training chips, excluded events from model fitting, and measured computational cost and prediction reliability. With 252 training chips, U-Net achieved a water intersection-over-union (IoU) of 0.856 on the official test set, compared with 0.833 for fine-tuned Prithvi-Tiny. Pretraining improved the tiny transformer over random initialization by a reported 0.046 IoU with 25 training chips and 0.023 with 252 chips, but did not reverse the U-Net ranking. Across 11 event-exclusion evaluations, mean IoU was 0.768 for fine-tuned Prithvi-Tiny and 0.777 for U-Net; the paired interval included zero. Fine-tuned Prithvi-Tiny processed a 512x512-pixel chip in 16.1 ms on a GPU and 258 ms on a laptop CPU, compared with 30.6 and 665 ms for U-Net. Errors were concentrated near water boundaries, and high predictive entropy identified many, but not all, erroneous pixels. These results support a specific role for compact pretrained models: lower measured computational demand with competitive accuracy, rather than consistent superiority. Event-dependent performance, shared hyperparameter selection, and the distinction between observed water and newly inundated land delimit the conclusions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34904",
  "author_total": 2,
  "authors": [
    {
      "name": "Parastoo Farajpoor"
    },
    {
      "name": "Mohammadreza Narimani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3w0emd1tcjh3r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34904",
  "fulltext_url": null,
  "id": "3w0emd1tcjh3r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34904",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2306|Flood Risk Assessment and Management"
    ]
  ],
  "openalex": "W7223794972",
  "pdf_url": "https://arxiv.org/pdf/2610.34904",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3w0emd1tcjh3r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:49Z",
      "native_id": "2610.34904",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34904"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Accuracy and efficiency of compact foundation models for surface-water mapping during floods",
  "title_text": "Accuracy and efficiency of compact foundation models for surface-water mapping during floods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:49Z",
  "url": "https://research.draconitic.com/paper/3w0emd1tcjh3r",
  "venue": "arXiv",
  "versions": []
}