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mmla_opc

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10K<n<100K
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biology
image
animals
CV
drone
zebra
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metadata 
license: cc0-1.0
language:
  - en
pretty_name: wildwing_opc
task_categories:
  - image-classification
tags:
  - biology
  - image
  - animals
  - CV
  - drone
  - zebra
size_categories: 10K<n<100K

Dataset Card for wildwing-opc

Dataset Details

This is a dataset containing annotated video frames of Plains zebras collected at the Ol Pejeta Conservancy (OPC) in Kenya using the autonomous WildWing system. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery. The dataset includes frames from various sessions, with annotations indicating the presence of zebras in the images in COCO format. The dataset is designed to facilitate research in wildlife monitoring and conservation using advanced imaging technologies.

Dataset Description

This dataset contains video frames collected from the WildWing system, which is an autonomous drone designed for wildlife monitoring.

The dataset includes frames from multiple sessions, over two days of data collection, 2025-01-31 and 2025-02-01, with a total of 5 videos. Each session captures video footage of Plains zebras in their natural habitat at the Ol Pejeta Conservancy in Kenya.

The dataset consists of 29,268 frames. Each frame is accompanied by annotations in COCO format, indicating the presence of zebras and their bounding boxes within the images. The annotations were completed manually by the dataset curator using CVAT and kabr-tools.

The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.

Session Date Collected Video ID Total Frames Size (pixels)
session_1 2025-01-31 P0800081 5,949 3840x2160
session_1 2025-01-31 P0830086 2,439 3840x2160
session_1 2025-01-31 P0840087 4,461 4096x2160
session_1 2025-01-31 P0860090 1,754 3840x2160
session_1 2025-01-31 P0870091 2,123 4096x2160
session_2 2025-02-01 P0910095 5,978 4096x2160
session_2 2025-02-01 P0940098 6,564 4096x2160
Total Frames: 29,268

Dataset Structure 

/dataset/
    <session_1>/
        classes.txt>
        <video_id>_000000.jpg
        <video_id>_000000.txt
        <video_id>_000001.jpg
        ...
        <video_id>_<frame_number>.jpg
        <video_id>_<frame_number>.txt
    <session_2>/
        classes.txt
        <video_id>_000000.jpg
        <video_id>_000000.txt
        <video_id>_000001.jpg
        ...
        <video_id>_<frame_number>.jpg
        <video_id>_<frame_number>.txt

Data Instances

All images are names _.jpg, each within a folder named for the date of the session. The annotations are in COCO format and are stored in a corresponding .txt file with the same name as the image. 2025-01-31 and 2025-02-01 are the two days of data collection, with a total of 7 sessions. 2025-01-31 has 5 sessions and 2025-02-01 has 2 sessions.

Data Fields

classes.txt:

  • 0: zebra

frame_id.txt:

  • class: Class of the object in the image (0 for zebra)
  • x_center: X coordinate of the center of the bounding box (normalized to [0, 1])
  • y_center: Y coordinate of the center of the bounding box (normalized to [0, 1])
  • width: Width of the bounding box (normalized to [0, 1])
  • height: Height of the bounding box (normalized to [0, 1])

Dataset Creation

Curation Rationale

The dataset was created to facilitate research in wildlife monitoring and conservation using advanced imaging technologies. The goal is to develop and evaluate computer vision models that can accurately detect and classify animals from drone imagery, and their generalizability across different species and environments.

Data Collection and Processing

The data was collected using the WildWing system, which autonomously captures video footage of wildlife in their natural habitat. The data collection process involved flying the drone over the Ol Pejeta Conservancy in Kenya, where Plains zebras were observed. The missions were flown during the WildDrone Hackathon in January 2025, with the goal of capturing high-quality video footage for analysis.

The videos were annotated manually using the Computer Vision Annotation Tool CVAT and kabr-tools library. These detection annotations and original video files were then processed to extract individual frames, which were saved as JPEG images. The annotations were converted to COCO format, with bounding boxes indicating the presence of zebras in each frame.

Annotations

Annotation process

CVAT and kabr-tools were used to annotate the video frames. The annotation process involved manually labeling the presence of zebras in each frame, drawing bounding boxes around them, and converting the annotations to COCO format.

Who are the annotators?

Jenna Kline

Personal and Sensitive Information

The dataset was cleaned to remove any personal or sensitive information. All images are of Plains zebras in their natural habitat, and no identifiable human subjects are present in the dataset.

Licensing Information

This dataset (the compilation) has been marked as dedicated to the public domain by applying the CC0-1.0 Public Domain Waiver. However, images may be licensed under different terms (as noted above).

Citation

BibTeX:

Data

@misc{wildwing_opc,
  author = {Kline, Jenna and 
            Nguyen Ngoc, Dat and
            Hine, Duncan and 
            Rondeau Saint-Jean, Camille and
            Maalouf, Guy and
            Juma, Brenda and
            Kilwaya, Alex and
            Vuyiya, Brian and
            Macharia, Irungu and
            Njoroge, William and
            Mutisya, Samuel and
            Guerin, David and
            Costelloe, Blair and
            Pastucha, Elzbieta and
            Hermansen, Jussi and
            Jensen, Kjeld and
            Watson, Matt and
            Richardson, Tom and
            Pagh Schultz Lundquist, Ulrik  
          },
  title = {WildWing Ol Pejeta Conservancy (OPC) Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/wildwing-opc},
  doi = {<doi once generated>},
  publisher = {Hugging Face}
}

Acknowledgements

This work was supported by the WildDroneEU Project. WildDrone is an MSCA Doctoral Network funded by the European Union’s Horizon Europe research and innovation funding programme under the Marie Skłodowska-Curie grant agreement no. 101071224.

This work was supported by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).

This work was supported by the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment ICICLE, which is funded by the US National Science Foundation under grant number OAC-2112606.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

More Information

The data was collected under Kenya Civil Aviation Authority (KCAA) permit number KCAA/UAS/OPS/0048/2025. The data collection was conducted in collaboration with the Ol Pejeta Conservancy and the WildDrone Hackathon team in accordance with Research License No. NACOSTI/P/25/415376.

Dataset Card Authors

Jenna Kline

Dataset Card Contact

kline.377 at osu.edu