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


# Dataset Card for wildwing-mpala

<!-- Provide a quick summary of what the dataset is or can be used for. --> 

## 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.


### Dataset Description

- **Curated by:** Jenna Kline
- **Homepage:** [mmla](https://imageomics.github.io/mmla/)
- **Repository:** [https://github.com/imageomics/mmla](https://github.com/imageomics/mmla)
- **Papers:** 

  - [MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset](https://arxiv.org/abs/2504.07744)

  - [WildWing: An open-source, autonomous and affordable UAS for animal behaviour video monitoring](https://doi.org/10.1111/2041-210X.70018)

  - [Deep Dive KABR](https://doi.org/10.1007/s11042-024-20512-4)

  - [KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos](https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf)



<!-- Provide a longer summary of what this dataset is. -->
This dataset contains video frames collected as part of the [Kenyan Animal Behavior Recognition (KABR)](https://kabrdata.xyz/) project at the [Mpala Research Center](https://mpala.org/) in Kenya in January 2023. 

Sessions 1 and 2 are part of the [original KABR data release](https://huggingface.co/datasets/imageomics/KABR), 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](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/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 <video_id_frame>.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])

<!-- ### Data Splits
[More Information Needed] -->
<!--
Give your train-test splits for benchmarking; could be as simple as "split is indicated by the `split` column in the metadata file: `train`, `val`, or `test`." Or perhaps this is just the training dataset and other datasets were used for testing (you may indicate which were used).
-->

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. For instance, what you intended to study and why that required curation of a new dataset (or if it's newly collected data and why the data was collected (intended use)), etc. -->

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

<!-- This section describes the source data (e.g., news text and headlines, social media posts, translated sentences, ...). As well as an original source it was created from (e.g., sampling from Zenodo records, compiling images from different aggregators, etc.) -->
Please see the original [KABR dataset](https://huggingface.co/datasets/imageomics/KABR) for more information on the source data.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, re-sizing of images, tools and libraries used, etc. 
This is what _you_ did to it following collection from the original source; it will be overall processing if you collected the data initially.
-->

The data was collected manually using a [DJI Air 2S drone](https://www.dji.com/support/product/air-2s). The drone was flown at the [Mpala Research Center](https://mpala.org/) 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](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/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.

<!-- #### Who are the source data producers?
[More Information Needed] -->
<!-- This section describes the people or systems who originally created the data.

Ex: This dataset is a collection of images taken of the butterfly collection housed at the Ohio State University Museum of Biological Diversity. The associated labels and metadata are the information provided with the collection from biologists that study butterflies and supplied the specimens to the museum.
 -->


### Annotations
<!-- 
If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. 

Ex: We standardized the taxonomic labels provided by the various data sources to conform to a uniform 7-rank Linnean structure. (Then, under annotation process, describe how this was done: Our sources used different names for the same kingdom (both _Animalia_ and _Metazoa_), so we chose one for all (_Animalia_). -->


#### Annotation process
[CVAT](https://www.cvat.ai/) and [kabr-tools](https://github.com/Imageomics/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.
<!-- This section describes the annotation process such as annotation tools used, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->
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.
<!-- 
For instance, if your data includes people or endangered species. -->


<!-- ## Considerations for Using the Data
[More Information Needed]
<!--
Things to consider while working with the dataset. For instance, maybe there are hybrids and they are labeled in the `hybrid_stat` column, so to get a subset without hybrids, subset to all instances in the metadata file such that `hybrid_stat` is _not_ "hybrid".
-->

<!-- ### Bias, Risks, and Limitations
[More Information Needed] -->
<!-- This section is meant to convey both technical and sociotechnical limitations. Could also address misuse, malicious use, and uses that the dataset will not work well for.-->

<!-- For instance, if your data exhibits a long-tailed distribution (and why). -->

<!-- ### Recommendations
[More Information Needed]
This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

## Licensing Information

This dataset (the compilation) has been marked as dedicated to the public domain by applying the [CC0-1.0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above).


## Citation


**BibTeX:**


**Data**
```​
@misc{wildwing_opc,
  author = {  Jenna Kline,
              Guy Maalouf,
              Camille Rondeau Saint-Jean,
              Dat Nguyen Ngoc,
              Majid Mirmehdi,
              David Guerin,
              Tilo Burghardt,
              Elzbieta Pastucha,
              Blair Costelloe,
              Matthew Watson,
              Thomas Richardson,
              Ulrik Pagh Schultz Lundquist
          },
  title = {WildWing Ol Pejeta Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/wildwing-opc},
  doi = {<doi once generated>},
  publisher = {Hugging Face}
}
```

**Papers**

```​
@article{kline2025mmla,
  title={MMLA: Multi-Environment, Multi-Species, Low-Altitude Aerial Footage Dataset},
  author={Kline, Jenna and Stevens, Samuel and Maalouf, Guy and Saint-Jean, Camille Rondeau and Ngoc, Dat Nguyen and Mirmehdi, Majid and Guerin, David and Burghardt, Tilo and Pastucha, Elzbieta and Costelloe, Blair and others},
  journal={arXiv preprint arXiv:2504.07744},
  year={2025}
}
```



## Acknowledgements

This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=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](https://icicle.osu.edu/), which is funded by the US National Science Foundation under grant number OAC-2112606.

<!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. -->

<!-- ## Glossary  -->

<!-- [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

## 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.

<!-- [optional] Any other relevant information that doesn't fit elsewhere. -->

## Dataset Card Authors 

Jenna Kline

## Dataset Card Contact

kline.377 at osu.edu
<!-- Could include who to contact with questions, but this is also what the "Discussions" tab is for. -->