Datasetpublic

edouard-rolland/zebra-herds-aerial

Dataset Description This dataset presents labelled top-view (nadir) aerial images of plains zebra (Equus quagga) herds. The footage was captured with a DJI Mini-series drone flying at an altitude of 60 metres, in January 2025 during a field campaign at the Ol Pejeta Conservancy, Laikipia County, Kenya. Individual video frames were extracted and every visible zebra was annotated with a bounding box, making the dataset suitable for training and evaluating object-detection… See the full description on the dataset page: https://huggingface.co/datasets/edouard-rolland/zebra-herds-aerial.

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Dataset Card

Dataset Description

This dataset presents labelled top-view (nadir) aerial images of plains zebra (Equus quagga) herds. The footage was captured with a DJI Mini-series drone flying at an altitude of 60 metres, in January 2025 during a field campaign at the Ol Pejeta Conservancy, Laikipia County, Kenya. Individual video frames were extracted and every visible zebra was annotated with a bounding box, making the dataset suitable for training and evaluating object-detection models for the automated detection and counting of zebras in drone imagery.

The data was collected as part of the **WildDrone** project, which investigates the use of autonomous drones and drone swarms for non-intrusive monitoring of large mammals in their natural habitats.

Annotations are provided as COCO-style bounding boxes [x, y, width, height] in absolute pixel coordinates, stored under the objects field of each example.

Citation

@InProceedings{10.1007/978-3-032-07638-0_22,
  author    = {Rolland, Edouard G. A. and Meier, Kilian and Gr{\o}ntved, Kasper A. R. and Laporte-Devylder, Lucie and Maalouf, Guy and Lundquist, Ulrik P. S. and Christensen, Anders L.},
  editor    = {Mathieu, Philippe and De la Prieta, Fernando},
  title     = {Drone Swarms for Multi-perspective Monitoring of Large Mammals in their Natural Habitats: Deployment and Field Trials},
  booktitle = {Advances in Practical Applications of Agents, Multi-Agent Systems, and Computational Social Science: The PAAMS Collection},
  year      = {2025},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {266--277},
  isbn      = {978-3-032-07638-0}
}

Acknowledgement

This work is supported by the WildDrone MSCA Doctoral Network funded by EU Horizon Europe under grant agreement no. 101071224, and by the Innovation Fund.

Dataset Labels

['zebra']

Example of Labelled Images

Annotated Zebras

The image below is a patchwork of every annotated zebra in the dataset — each tile is a single bounding-box crop. It illustrates the scale of the 6,159 annotations and the variety of poses, herd densities, and backgrounds captured from a top-view drone perspective.

Number of Images

json

{'train': 424, 'validation': 107}

Intended Uses

The dataset is intended for training and evaluating object-detection models (for example the YOLO family) for the automated detection and counting of zebras in top-view drone imagery. Such models support non-intrusive wildlife monitoring and conservation efforts. More context on the drone-swarm monitoring system behind this data collection can be found in the paper listed in the Citation section.

How to Use

—Install datasets:

bash

pip install datasets

—Load the dataset:

python

from datasets import load_dataset

ds = load_dataset("edouard-rolland/zebra-herds-aerial")
example = ds['train'][0]

License

MIT