Adrian Baggström

PhD student at Department of Organismal Biology; Systematic Biology

Telephone:: +46 18 471 27 74
E-mail:: adrian.baggstrom@ebc.uu.se
Visiting address:: Evolutionsbiologiskt centrum, Norbyv. 18D
75236 Uppsala
Postal address:: Norbyv. 18D
75236 Uppsala

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Short presentation

I am a PhD student focusing on the fields of biodiversity, geomatics, remote sensing and machine learning. My interest is to research the possibilities of assessing biodiversity through remote sensing techniques such as spectral imagery and lidar. By combining these techniques with existing geospatial data and deep learning I aim to develop tools that contribute to the monitoring of biodiversity at different scales.

Keywords

biodiversity
remote sensing
modeling
machine learning

Publications

Recent publications

Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data

Gomes De Almeida, Filipe; Baggström, Adrian; Andermann, Tobias

Part of Ecological Informatics, 2025
- DOI for Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data
- Download full text (pdf) of Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data
The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale

Baggström, Adrian; Goodsell, Robert; van Dijk, Laura; Iwaszkiewicz-Eggebrecht, Ela et al.

Part of Ecological Informatics, 2025
- DOI for The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale
- Download full text (pdf) of The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale

All publications

Articles in journal

Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data

Gomes De Almeida, Filipe; Baggström, Adrian; Andermann, Tobias

Part of Ecological Informatics, 2025
- DOI for Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data
- Download full text (pdf) of Estimating national Red List statuses for fungi in Sweden: An improved deep learning approach to account for unbalanced data
The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale

Baggström, Adrian; Goodsell, Robert; van Dijk, Laura; Iwaszkiewicz-Eggebrecht, Ela et al.

Part of Ecological Informatics, 2025
- DOI for The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale
- Download full text (pdf) of The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale