README.md

---
license: cc0-1.0
task_categories:
- robotics
language:
- en
tags:
- drone
- ecology
- zebra
- Grevy's zebra
- conservation
- UAV
size_categories:
- 100K<n<1M

configs:
- config_name: telemetry_data
  data_files: "consolidated_metadata.csv"
  default: true
- config_name: column_information
  data_files: "kabr_telemetry_metadata.csv"
---
# Dataset Card for KABR Telemetry: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos

## Dataset Details

### Dataset Description

This dataset contains the drone telemetry data associated with the [KABR](https://huggingface.co/datasets/imageomics/KABR) dataset. The KABR dataset contains annotated video behavior of zebras and giraffes at the Mpala Research Centre. This telemetry dataset contains information about the status drone during the missions, including location and altitude, along with the bounding box dimensions of the wildlife in the frame and behavior annotation information. Please see the "kabr_telemetry_metadata.csv" for more details.

- **Paper:** [Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs)](https://jennamk14.github.io/images/Integrating%20Biological%20Data%20into%20Autonomous%20Remote%20Sensing%20Systems%20for%20In%20Situ%20Imageomics-%20A%20Case%20Study%20for%20Kenyan%20Animal%20Behavior%20Sensing%20with%20Unmanned%20Aerial%20Vehicles%20(UAVs).pdf)
- **Point of Contact:** Jenna Kline, kline.377@osu.edu 

- **Curated by:** Jenna Kline, Maksim Kholiavchenko, Otto Brookes, Tanya Berger-Wolf, Charles V. Stewart, and Christopher Stewart
- **Funded by:** Imageomics
- **Shared by:** Jenna Kline

## Uses

This dataset is intended to be used to provide guidance on executing wildlife behavior collection missions with drones, which can be conducted by drone pilots manually, or integrated into an autonomous navigation framework.

## Dataset Creation

### Curation Rationale

This dataset was curated to provide additional context to the KABR dataset, and provide spatial information which can be used to develop autonomous navigation algorithms for wildlife data collection.


#### Data Collection and Processing

This data was collected at the Mpala Research Centre in Laikipia, Kenya in January 2023. A DJI Mavic Air 2 drone was used to collect the data, and [AirData](https://airdata.com/) was used to process DJI telemetry files.

### Annotations

Please refer to the [KABR]((https://huggingface.co/datasets/imageomics/KABR)) dataset and associated paper for details on the annotation process.

## Additional Information

### Authors

* Jenna Kline (The Ohio State University)
* Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
* Otto Brookes (University of Bristol)
* Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412
* Charles V. Stewart (Rensselaer Polytechnic Institute)
* Christopher Stewart (The Ohio State University)

### Licensing Information

This dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research.


### Citation Information

#### Dataset
```
@misc{KABR_telemetry,
  author = {Kline, Jenna, Kholiavchenko, Maksim and Berger-Wolf, Tanya and Stewart, Charles V. and Stewart, Christopher},
  title = {KABR Telemetry},
  year = {2024},
  url = {https://huggingface.co/datasets/imageomics/KABR-telemetry},
  doi = {doi:10.57967/hf/1745},
  publisher = {Hugging Face}
}
```
#### Paper
```
@inproceedings{kline_kabr_telemetry,
  title={Integrating Biological Data into Autonomous Remote Sensing Systems for In Situ Imageomics: A Case Study for Kenyan Animal Behavior Sensing with Unmanned Aerial Vehicles (UAVs)},
  author={Kline, Jenna and Kholiavchenko, Maksim and Berger-Wolf, Tanya and Stewart, Charles V. and Stewart, Christopher}},
  booktitle={Proceedings of the First Workshop on Imageomics: Discovering Biological Knowledge from Images using AI, held as part of AAAI 24},
  year={2024}
}
```

### Contributions

The [Imageomics Institute](https://imageomics.org) 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.