Biogeosciences
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Articles | Volume 22, issue 21
Articles | Volume 22, issue 21
https://doi.org/10.5194/bg-22-6545-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-6545-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 21
Research article
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06 Nov 2025
Research article | Highlight paper |  | 06 Nov 2025

Automated mask generation in citizen science smartphone photos and their value for mapping plant species in drone imagery

Salim Soltani, Lauren E. Gillespie, Moises Exposito-Alonso, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn

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Editorial statement
Soltani et al. present an automated workflow for transforming weakly annotated citizen science plant photographs into robust training data for drone-based remote sensing of vegetation. Their approach demonstrates that plant images collected by non-specialists can be effectively leveraged in machine learning models to improve the accuracy of species mapping using drones.
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Short summary
We introduce an automated approach for generating segmentation masks for citizen science plant photos, making them applicable to computer vision models. This framework effectively transforms citizen science data into a data treasure for segmentation models for plant species identification in aerial imagery. Using automatically labeled photos, we train segmentation models for mapping tree species in drone imagery, showcasing their potential for forestry, agriculture, and biodiversity monitoring.
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