Evidence map›Paper›PMID 39695144›Full record

ArticleScientific data2024

SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution.

Cesar Aybar, David Montero, Julio Contreras, Simon Donike, Freddie Kalaitzis, Luis Gómez-Chova

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cesar AybarImage Processing Laboratory, University of Valencia, Valencia, Spain. cesar.aybar@uv.es.ORCID http://orcid.org/0000-0003-2745-9535
David MonteroInstitute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany.ORCID http://orcid.org/0000-0002-9010-3286
Julio ContrerasImage Processing Laboratory, University of Valencia, Valencia, Spain.
Simon DonikeImage Processing Laboratory, University of Valencia, Valencia, Spain.
Freddie KalaitzisOxford Applied and Theoretical ML Group, University of Oxford, Oxford, UK.
Luis Gómez-ChovaImage Processing Laboratory, University of Valencia, Valencia, Spain.ORCID http://orcid.org/0000-0003-3924-1269

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing demand for high spatial resolution in remote sensing has underscored the need for super-resolution (SR) algorithms that can upscale low-resolution (LR) images to high-resolution (HR) ones. To address this, we present SEN2NAIP, a novel and extensive dataset explicitly developed to support SR model training. SEN2NAIP comprises two main components. The first is a set of 2,851 LR-HR image pairs, each covering 1.46 square kilometers. These pairs are produced using LR images from Sentinel-2 (S2) and corresponding HR images from the National Agriculture Imagery Program (NAIP). Using this cross-sensor dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of S2 imagery (

Identifiers

PMID39695144
PMCPMC11655869

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.