A Drone Imagery Dataset for Semantic Segmentation of Urban Garden Ground Covers in Biodiversity Studies

Abstract

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%andIntersection 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.

Yasamin Afrasiabian

PhD student

My research interests include remote sensing, particularly hyperspectral UAV and satellite imaging, and machine-learning methods for ecosystem-biodiversity characterisation, precision agriculture, and hydrological analysis.

Chenghao Lu

Research assistant & Joint Phd student

My research interests are Agricultural Digital Twin and Smart Breeding. My project focuses on the construction of a Digital Twin system for maize, aiming to fuse Genetic x Environmental x Management data to construct a high-precision and dynamically updated maize growth virtual twin. By introducing a DNA language model with a multimodal fusion approach, I hope to achieve accurate simulation of maize phenotypes and breeding to support sustainable agricultural development.

Dr. Anirudh Belwalkar

Postdoctoral Researcher

My research interests include hyperspectral remote sensing of vegetation and precision agriculture.

Hany El Sharawy

PhD Student

My research focuses on UAV-based phenotyping, hyperspectral remote sensing, and high-throughput phenotyping of maize under drought conditions.

Xiaoxin Song

PhD Student

My research interests include multi-scale plant and crop phenotyping, crop growth and senescence monitoring, dynamic modelling, and remote sensing.

Ying Yuan

PhD student

My research focuses on remote sensing phenotyping of winter wheat, including ground-based and UAV-based hyperspectral remote sensing, solar-induced chlorophyll fluorescence remote sensing…

Fei Wu

PhD Student

My research focuses on UAV remote sensing and precision agriculture.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.