column_name stringlengths 2 24	description stringlengths 9 69
frame	frame number (30 fps)
id	unique identifier for each animal detected in video frame
date_time	date time
latitude_y	latitude (GPS)
longitude_y	longitude (GPS)
height_sonar(feet)	drone altitude in feet
compass_heading(degrees)	drone compass heading in degrees
pitch(degrees)	drone pitch in degrees
roll(degrees)	drone roll in degrees
gimbal_heading(degrees)	camera gimbal heading in degrees
gimbal_pitch(degrees)	camera gimbal pitch in degrees
gimbal_roll(degrees)	camera gimbal roll in degrees
xtl	top left x coordinate of bounding box
ytl	top left y coordinate of bounding box
xbr	bottom right x coordinate of bounding box
ybr	bottom right y coordinate of bounding box
label	animal species (e.g. zebra, giraffe, etc.)
behaviour	behaviour of animal
source	source of bounding box annotations (e.g. manual, deep learning, etc.)
mission id	unique mission id with date and start time
altitude (m)	drone altitude in meters

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 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)
Point of Contact: Jenna Kline, [email protected]
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 was used to process DJI telemetry files.

Annotations

Please refer to the 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 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.

Datasets:

imageomics
/

KABR-telemetry