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Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in E...

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in E...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_3069280622

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

About this item

Full title

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

Publisher

San Francisco: Public Library of Science

Journal title

PloS one, 2023-11, Vol.18 (11), p.e0292839-e0292839

Language

English

Formats

Publication information

Publisher

San Francisco: Public Library of Science

More information

Scope and Contents

Contents

Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not clear how these models rank in this task. In this study, these machine learning models were evaluated...

Alternative Titles

Full title

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_plos_journals_3069280622

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_3069280622

Other Identifiers

ISSN

1932-6203

E-ISSN

1932-6203

DOI

10.1371/journal.pone.0292839

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