Author

E. Guirado

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:imaging· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

D. Alcaraz-Segura· 2 shared F. Herrera· 2 shared S. Tabik· 2 shared B. Achchab· 1 shared B. K. Singh· 1 shared C. COLEINE· 1 shared D. Albanese· 1 shared E. Egidi· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersEcology

D. Alcaraz-Segura

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Author profile →

3 papers

Recent·Most cited

Environmental thresholds in the functional mycobiome of global drylands

E. Egidi, M. Delgado-Baquerizo, M. Berdugo, E. Guirado, D. Albanese, B. K. Singh, C. COLEINE

The paper analyzes fungal community data from global drylands to identify environmental thresholds affecting community composition and functional guilds.

bioRxiv · Mar 1, 2022 · PreprintEcology

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

Y. Benhammou, D. Alcaraz-Segura, E. Guirado, R. Khaldi, B. Achchab, F. Herrera, S. Tabik

The paper introduces a Sentinel-2 RGB image dataset for global land use/cover mapping, optimized for deep learning to improve consistency and accuracy in LULC classification.

bioRxiv · Dec 1, 2021 · PreprintEcology

Automatic whale counting in satellite images with deep learning

E. Guirado, S. Tabik, M. L. Rivas, D. Alcaraz-Segura, F. Herrera

This paper develops a deep learning system for automatically counting whales from satellite images, achieving 84% detection and 97% counting accuracy in global whale-watching hotspots.

bioRxiv · Oct 16, 2018Ecology