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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Dataset of very-high-resolution satellite RGB images to train deep learning models to recognize high-mountain juniper shrubs from Sierra Nevada (Spain)

Authors: Khaldi, Rohaifa; Puertas, Sergio; Tabik, Siham; Alcaraz-Segura, Domingo;

Dataset of very-high-resolution satellite RGB images to train deep learning models to recognize high-mountain juniper shrubs from Sierra Nevada (Spain)

Abstract

This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to recognize Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 2000 images (.jpg) of size 512x512 pixels partitioned into two classes: Shrubs and NoShrubs. We also provide partitioning of the data into Train (1800 images), Test (100 images), and Validation (100 images) subsets.

This research has been supported by DETECTOR (A-RNM-256-UGR18 Universidad de Granada/FEDER), and LifeWatch SmartEcomountains (LifeWatch-2019-10-UGR-01 Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER).

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Keywords

Image classification, Sierra Nevada (Spain), Deep learning, High-mountain shrubs, Biodiversity, Satellite images, Remote sensing, Google Earth images

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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