Robust U-Net Segmentation of Tree Crown Damages in Bavaria, Germany

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated.We trained the models with imagery from May to September for the years 2019-2023.One goal was to differentiate between damaged crowns of deciduous, coniferous, and pine trees.The results from a validation area containing an independent dataset showed the best average F1-scores of 68%, 52%, and 66% for deciduous, coniferous, and pine trees, respectively.This study highlights the potential of U-Net methods for detecting tree mortality in large areas.

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Published
2025-07-25
DOI
https://doi.org/10.3390/engproc2025094012
Primary Topic
Forest ecology and management
Type
article
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article

Robust U-Net Segmentation of Tree Crown Damages in Bavaria, Germany

Adelheid Wallner, Jessenia Gonzalez
Forest ecology and management
article

Robust U-Net Segmentation of Tree Crown Damages in Bavaria, Germany

Adelheid Wallner, Jessenia Gonzalez
article en

Abstract

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated.We trained the models with imagery from May to September for the years 2019-2023.One goal was to differentiate between damaged crowns of deciduous, coniferous, and pine trees.The results from a validation area containing an independent dataset showed the best average F1-scores of 68%, 52%, and 66% for deciduous, coniferous, and pine trees, respectively.This study highlights the potential of U-Net methods for detecting tree mortality in large areas.

Bavarian State Research Center for Agriculture (DE)
Openalex Percentile: Top 80%
Forest ecology and management
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