imageomics/wildwing_mpala

Dataset Card for wildwing-mpala

Dataset Details

This is a dataset containing annotated video frames of giraffes, Grevy's zebras, and Plains zebras collected at the Mpala Research Center in Kenya. The dataset is intended for use in training and evaluating computer vision models for animal detection and classification from drone imagery.

The annotations indicate the presence of animals in the images in COCO format. The dataset is designed to facilitate research in wildlife monitoring and conservation using autonomous drones.

This dataset contains video frames collected as part of the Kenyan Animal Behavior Recognition (KABR) project at the Mpala Research Center in Kenya in January 2023.

Sessions 1 and 2 are part of the original KABR data release, now available in COCO format. Sessions 3, 4 and 5 are part of the extended release. 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.

The dataset consists of 104,062 frames. Each frame is accompanied by annotations in COCO format, indicating the presence of zebras and giraffes and their bounding boxes within the images. The annotations were completed manually by the dataset curator using CVAT and kabr-tools.

Session	Date Collected	Total Frames	Species	Video File IDs in Session
session_1	2023-01-12	16,891	Giraffe	DJI_0001, DJI_0002
session_2	2023-01-17	11,165	Plains zebra	DJI_0005, DJI_0006
session_3	2023-01-18	17,940	Grevy's zebra	DJI_0068, DJI_0069, DJI_0070, DJI_0071
session_4	2023-01-20	33,960	Grevy's zebra	DJI_0142, DJI_0143, DJI_0144, DJI_0145, DJI_0146, DJI_0147
session_5	2023-01-21	24,106	Giraffe, Plains and Grevy's zebras	DJI_0206, DJI_0208, DJI_0210, DJI_0211
Total Frames:		104,062

This table shows the data collected at Mpala Research Center in Laikipia, Kenya, with session information, dates, frame counts, and primary species observed.

The dataset includes frames extracted from drone videos captured during five distinct data collection sessions. Each session represents a separate field excursion lasting approximately one hour, conducted at a specific geographic location. Multiple sessions may occur on the same day but in different locations or targeting different animal groups. During each session, multiple drone videos were recorded to capture animals in their natural habitat under varying environmental conditions.

Dataset Structure

/dataset/
    classes.txt
    session_1/
        DJI_0001/
            partition_1/
              DJI_0001_000000.jpg
              DJI_0001_000001.txt
              ...
              DJI_0001_004999.txt
            partition_2/
              DJI_0001_005000.jpg
              DJI_0001_005000.txt
              ...
              DJI_0001_008700.txt
        DJI_0002/
          DJI_0002_000000.jpg
          DJI_0002_000001.txt
          ...
          DJI_0002_008721.txt
        metadata.txt
    session_2/
        DJI_0005/
          DJI_0005_001260.jpg
          DJI_0005_001260.txt
          ...
          DJI_0005_008715.txt
        DJI_0006/
          partition_1/
            DJI_0006_000000.jpg
            DJI_0006_000001.txt
            ...
            DJI_0006_005351.txt
          partition_2/
            DJI_0006_005352.jpg
            DJI_0006_005352.txt
            ...
            DJI_0006_008719.txt
        metadata.txt
    session_3/
        DJI_0068/
          DJI_0068_000780.jpg
          DJI_0068_000780.txt
          ...
          DJI_0068_005790.txt
        DJI_0069/
          partition_1/
            DJI_0069_000000.jpg
            DJI_0069_000001.txt
            ...
            DJI_0069_004999.txt
          partition_2/
            DJI_0069_005000.jpg
            DJI_0069_005000.txt
            ...
            DJI_0069_005815.txt
        DJI_0070/
          partition_1/
            DJI_0070_000000.jpg
            DJI_0070_000001.txt
            ...
            DJI_0069_004999.txt
          partition_2/
            DJI_0070_005000.jpg
            DJI_0070_005000.txt
            ...
            DJI_0070_005812.txt
        DJI_0071/
          DJI_0071_000000.jpg
          DJI_0071_000000.txt
          ...
          DJI_0071_001357.txt
        metadata.txt
    session_4/
        DJI_0142/
            partition_1/
                DJI_0142_000000.jpg
                DJI_0142_000000.txt
                ...
                DJI_0142_002999.txt
            partition_2/
                DJI_0142_003000.jpg
                DJI_0142_003000.txt
                ...
                DJI_0142_005799.txt
        DJI_0143/
            partition_1/
                DJI_0143_000000.jpg
                DJI_0143_000000.txt
                ...
                DJI_0143_002999.txt
            partition_2/
                DJI_0143_003000.jpg
                DJI_0143_003000.txt
                ...
                DJI_0143_005816.txt
        DJI_0144/
            partition_1/
                DJI_0144_000000.jpg
                DJI_0144_000000.txt
                ...
                DJI_0144_002999.txt
            partition_2/
                DJI_0144_003000.jpg
                DJI_0144_003000.txt
                ...
                DJI_0144_005790.txt
        DJI_0145/
            partition_1/
                DJI_0145_000000.jpg
                DJI_0145_000000.txt
                ...
                DJI_0145_002999.txt
            partition_2/
                DJI_0145_003000.jpg
                DJI_0145_003000.txt
                ...
                DJI_0145_005811.txt
        DJI_0146/
            partition_1/
                DJI_0146_000000.jpg
                DJI_0146_000000.txt
                ...
                DJI_0146_002999.txt
            partition_2/
                DJI_0146_003000.jpg
                DJI_0146_003000.txt
                ...
                DJI_0146_005809.txt
        DJI_0147/
            partition_1/
                DJI_0147_000000.jpg
                DJI_0147_000000.txt
                ...
                DJI_0147_002999.txt
            partition_2/
                DJI_0147_003000.jpg
                DJI_0147_003000.txt
                ...
                DJI_0147_005130.txt
        metadata.txt
    session_5/
        DJI_0206/
            partition_1/
                DJI_0206_000000.jpg
                DJI_0206_000000.txt
                ...
                DJI_0206_002499.txt
            partition_2/
                DJI_0206_002500.jpg
                DJI_0206_002500.txt
                ...
                DJI_0206_004999.txt
            partition_3/
                DJI_0206_005000.jpg
                DJI_0206_005000.txt
                ...
                DJI_0206_005802.txt
        DJI_0208/
            partition_1/
                DJI_0208_000000.jpg
                DJI_0208_000000.txt
                ...
                DJI_0208_002999.txt
            partition_2/
                DJI_0208_003000.jpg
                DJI_0208_003000.txt
                ...
                DJI_0208_005810.txt
        DJI_0210/
            partition_1/
                DJI_0210_000000.jpg
                DJI_0210_000000.txt
                ...
                DJI_0210_002999.txt
            partition_2/
                DJI_0210_003000.jpg
                DJI_0210_003000.txt
                ...
                DJI_0210_005811.txt
        DJI_0211/
            partition_1/
                DJI_0211_000000.jpg
                DJI_0211_000000.txt
                ...
                DJI_0211_002999.txt
            partition_2/
                DJI_0211_003000.jpg
                DJI_0211_003000.txt
                ...
                DJI_0211_005809.txt
        metadata.txt

Data Instances

All images are named .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.

Note on data partitions: DJI saves video files into 3GB chunks, so each session is divided into multiple video files. HuggingFace limits folders to 10,000 files per folder, so each video file is further divided into partitions of 10,000 files. The partition folders are named partition_1, partition_2, etc. The original video files are not included in the dataset.

Data Fields

classes.txt:

• 0: zebra
• 1: giraffe
• 2: onager
• 3: dog

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.

Source Data

Please see the original KABR dataset for more information on the source data.

Data Collection and Processing

The data was collected manually using a DJI Air 2S drone. The drone was flown at the Mpala Research Center in Laikipia, Kenya, capturing video footage of giraffes, Grevy's zebras, and Plains zebras in their natural habitat.

The videos were annotated manually using the Computer Vision Annotation Tool CVAT and kabr-tools. 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 animals in each frame, drawing bounding boxes around them, and converting the annotations to COCO format.

Who are the annotators?

Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
Jenna Kline (The Ohio State University) - ORCID: 0009-0006-7301-5774
Michelle Ramirez (The Ohio State University)
Sam Stevens (The Ohio State University)
Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484
Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
Alison Zhong (The Ohio State University)

Personal and Sensitive Information

The dataset was cleaned to remove any personal or sensitive information. All images are of Plains zebras, Grevy's zebras, and giraffes 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_mpala,
  author = {  Jenna Kline,
              Maksim Kholiavchenko,
              Alison Zhong,
              Michelle Ramirez,
              Samuel Stevens,
              Nina Van Tiel,
              Elizabeth Campolongo,
              Matthew Thompson,
              Reshma Ramesh Babu,
              Namrata Banerji,
              Alec Sheets,
              Mia Magersupp,
              Sowbaranika Balasubramaniam,
              Isla Duporge,
              Jackson Miliko,
              Neil Rosser,
              Tanya Berger-Wolf,
              Eduardo Bessa,
              Charles V. Stewart,
              Daniel I. Rubenstein
          },
  title = {WildWing Mpala Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/wildwing-mpala},
  doi = {<doi once generated>},
  publisher = {Hugging Face}
}

Papers

@inproceedings{kholiavchenko2024kabr,
  title={KABR: In-situ dataset for kenyan animal behavior recognition from drone videos},
  author={Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and others},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={31--40},
  year={2024}
}

@article{kline2025wildwing,
  title={WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring},
  author={Kline, Jenna and Zhong, Alison and Irizarry, Kevyn and Stewart, Charles V and Stewart, Christopher and Rubenstein, Daniel I and Berger-Wolf, Tanya},
  journal={Methods in Ecology and Evolution},
  year={2025},
  doi={https://doi.org/10.1111/2041-210X.70018}
  publisher={Wiley Online Library}
}

Acknowledgements

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). 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.

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.

More Information

The data was gathered at the Mpala Research Center in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.

Dataset Card Authors

Jenna Kline

Dataset Card Contact

kline.377 at osu.edu