[Data & Code] A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies

Wed, 08 Apr 2026 00:00:00 +0000

This dataset accompanies the Data Descriptor published in Scientific Data by Afrasiabian et al. (2026), providing a high-resolution benchmark for mapping the fine-scale ground-cover heterogeneity of urban community gardens.

Urban gardens support city biodiversity through diverse ground covers that provide habitat, pollination, pest control, and soil functions — but their spatial heterogeneity has been poorly mapped due to a lack of high-resolution imagery. This dataset addresses that gap.

What’s in the dataset

24 RGB orthomosaics (GeoTIFF) and their 24 pixel-level label masks, collected in 2021–2022 at five community gardens in Munich, Germany
Organised into Train / Val / Test splits (14 / 5 / 5 orthomosaics and masks)
Spatial resolution 3.2–7.9 mm per pixel (18.9–146.4 megapixels; EPSG:25832)
Eight ground-cover classes: grass, herb, litter, soil, stone, straw, wood, and woodchip
A patch-based version (cropped image/mask patches) ready for deep-learning pipelines
A metadata CSV documenting per-image dimensions, resolution, CRS, and Agisoft processing/flight properties

Benchmarks

The descriptor benchmarks both deep-learning (UNet, DeepLabV3+) and traditional machine-learning (Random Forest, XGBoost, Maximum Likelihood) classifiers. DeepLabV3+ achieved the best performance, with an overall accuracy of ≈93.2% and an Intersection-over-Union of 69.4.

The dataset is intended to support research on urban biodiversity, habitat modelling, garden management, and remote sensing, and can be combined with other fine-scale datasets to advance sustainable urban green planning.

📦 Dataset (Zenodo): https://doi.org/10.5281/zenodo.18757882

💻 Code (GitHub): https://github.com/paglab/ugc-mapping

📄 Citation:

Afrasiabian, Y., Lu, C., Belwalkar, A., Elsharawy, H., Song, X., Yuan, Y., Wu, F., Su, X., Van Cleemput, E., Egerer, M., & Yu, K. (2026). A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies. Scientific Data, 13, 590. https://doi.org/10.1038/s41597-026-07152-z

Semantic Segmentation | Precision Agriculture Lab

[Data & Code] A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies

What’s in the dataset

Benchmarks