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ZENODO
Dataset . 2025
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 . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Post-deforestation Land Use in the Tropics (PLUTo)

Authors: Pisl, Jan; Zamora, Camilo; Herold, Martin; Sumbul, Gencer; Lenczner, Gaston; Wegner, Jan Dirk; Tuia, Devis;

Post-deforestation Land Use in the Tropics (PLUTo)

Abstract

Post-deforestation Land Use in the Tropics (PLUTo) PLUTo is a dataset of post-deforestation land uses across the tropics. It consists of locations deforested between 2000 and 2020 with known post-deforestation land use. To identify deforestation, we use the Tropical Moist Forest (TMF) dataset. Contents 1. labels.csv Columns: 'driver' ground truth label 'sampleid' unique identifier of every sample 'country' which country the sample lies in 'geometry' the coordinates of the center of the sample; each sample is 1x1km 'split' whether sample was used for training or evaluation 2. tabular_data.csv Columns: 'sampleid' unique identifier of every sample ['pasture', 'forest plantation', 'soy', 'coffee', 'palm oil', 'mining', 'urban', 'cocoa', 'rubber', 'shifting agriculture', 'other'] processed and normalized values from the DeDuCE dataset ( https://zenodo.org/records/10633818 ) 3. deforestation_masks/ - one file for each sample named {sampleid}.tif - masks indicating which pixels were deforestated within each image patch 4. images/ - one folder for each sample named {sampleid} - contains 4 Sentinel-2 images covering the location - images are slightly larger than the patch size to account for occasional geometric artefacts around image edges; they should be cropped to the same area as deforestation masks

Keywords

Tropical forest, Satellite image, Supervised Machine Learning, Deforestation, Remote sensing

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selected citations
These citations are derived from selected sources.
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.
BIP!Impulse provided by BIP!
0
Average
Average
Average