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A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies

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Abstract

Urban gardens promote urban biodiversity by providing diverse ground covers that support habitat provision, pollination, pest control, and soil functions. However, lacking high spatial resolution images, their spatial heterogeneity remains poorly mapped, limiting our understanding of how these features support ecosystem services. This study presents a high-resolution dataset derived from unmanned aerial vehicle (UAV) RGB imagery for the semantic segmentation of diverse ground covers in urban community gardens. The dataset consists of 2,521 images processed into 24 orthomosaics, acquired in 2021–2022 at five garden locations in Munich, Germany. Each image (18.9–146.4 M px; 3.2–7.9 mm resolution) is manually annotated into eight ground-cover classes (grass, herb, litter, soil, stone, straw, wood, and woodchip). We evaluated deep-learning segmentation models, including UNet and DeepLabV3+. The DeepLabV3+ (overall accuracy = 93.2% and Intersection over Union = 69.4) achieved high classification accuracy in distinguishing these complex classes. This dataset is intended to support research on urban biodiversity, habitat modelling, garden management, remote sensing research, and can be integrated with other fine-scale datasets to advance sustainable urban green planning.

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Data availability

The dataset generated by the current study is available on Zenodo53 (https://doi.org/10.5281/zenodo.18757882).

Code availability

The code is available on GitHub (https://github.com/paglab/ugc-mapping).

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Acknowledgements

This work was supported by the Hans Eisenmann-Forum für Agrarwissenschaften (HEF) Seed Fund 2021 and the NH3-Min project. The authors gratefully acknowledge the assistance of Ali Mokhtari, Jürgen Pluss, Wuhua Wang, and Jingcheng Zhang. During the preparation of this work the authors used ChatGPT to improve language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

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Open Access funding enabled and organized by Projekt DEAL.

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Authors and Affiliations

  1. Precision Agriculture Lab, TUM School of Life Sciences, Technical University of Munich, Dürnast 9, 85354, Freising, Germany

    Yasamin Afrasiabian, Anirudh Belwalkar, Hany Elsharawy, Xiaoxin Song, Ying Yuan, Fei Wu & Kang Yu

  2. Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland

    Chenghao Lu & Xiang Su

  3. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Xiang Su

  4. Leiden University College The Hague, Leiden University, Anna van Buerenplein 301, 2595 DG, Den Haag, The Netherlands

    Elisa Van Cleemput

  5. Urban Productive Ecosystems, TUM School of Life Sciences, Technical University of Munich, Hans Carl-von-Carlowitz-Platz 2, 85354, Freising, Germany

    Monika Egerer

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  1. Yasamin Afrasiabian
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  2. Chenghao Lu
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Contributions

Yasamin Afrasiabian: Investigation, Writing – Original Draft, Conceptualization, Validation. Chenghao Lu: Methodology, Visualization, Writing – Original Draft, Conceptualization. Anirudh Belwalkar: Conceptualization, Investigation, Validation. Hany Elsharawy: Methodology. Xiaoxin Song: Methodology. Ying Yuan: Methodology. Fei Wu: Methodology. Xiang Su: Methodology, Validation. Elisa Van Cleemput: Conceptualization, Writing – Review & Editing. Monika Egerer: Conceptualization, Recourses, Writing – Review & Editing. Kang Yu: Conceptualization, Methodology, Recourses, Writing – Review & Editing, Supervision.

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Kang Yu.

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Afrasiabian, Y., Lu, C., Belwalkar, A. et al. A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies.
Sci Data (2026). https://doi.org/10.1038/s41597-026-07152-z

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