fair_drones
FAIR² Drones Data Standard
A unified metadata standard for drone-based wildlife datasets
Overview
The FAIR² Drones Data Standard provides a comprehensive framework for documenting drone-based wildlife datasets, ensuring they are Findable, Accessible, Interoperable, and Reusable, AI-Ready and are compliant with Darwin Core biodiversity data standards. This standard bridges ecology, robotics, and computer vision communities by providing unified metadata specifications that enable cross-domain dataset reuse.
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Purpose
Field data collection using aerial and underwater drones represents a substantial investment in time, expertise, and resources. However, most datasets serve only single research communities, limiting interdisciplinary potential. The FAIR² Drones standard addresses this by:
- Standardizing metadata across ecology, robotics, and computer vision domains
- Integrating Darwin Core biodiversity standards for ecological compliance
- Documenting platform specifications essential for robotics research
- Specifying annotation formats required for AI/ML applications
- Enabling multimodal linkages to complementary sensor data
Key Features
- Modular template system supporting detection, tracking, behavior recognition, and robotics benchmarking
- Darwin Core compliance with Event and Occurrence records for GBIF integration
- Comprehensive platform metadata including telemetry, sensors, and mission parameters
- Multi-task annotation support for object detection, tracking, segmentation, and behavior analysis
- Validation tools for ensuring standard compliance
- Reference implementations demonstrating real-world applications
Repository Contents
- TEMPLATE.md: Full dataset card template with detailed field descriptions
- QUICKSTART_GUIDE.md: Checklist-based guide for rapid implementation
- examples/: Reference implementations on real-world datasets
- KABR Behavior Telemetry: Complete example with GPS extraction, Darwin Core events, and processing scripts
- Validation scripts: Tools for checking standard compliance (coming soon)
Getting Started
- Review the Quick-Start Guide for a checklist-based approach
- Select your template based on primary use case (detection, tracking, behavior, robotics)
- Complete the dataset card following the full template
- Validate compliance using provided tools
- Publish your dataset with FAIR² Drones documentation
Estimated completion time: 2-4 hours depending on dataset complexity
Standard Components
Core Metadata
- Dataset identification and attribution
- Licensing and citation information
- Data structure and file organization
- Dataset splits and statistics
Darwin Core Integration with Humbolt Extension
- Event records (survey locations, dates, protocols)
- Occurrence records (species observations, taxonomic hierarchy)
- Sampling effort and coverage metrics
- Geographic coordinates with uncertainty
Platform Specifications
- UAV/UUV hardware details
- Sensor specifications (camera, thermal, LiDAR, etc.)
- Flight parameters and telemetry
- Autonomy modes and mission planning
Annotation Documentation
- Task-specific formats (COCO, MOT, ethograms)
- Quality metrics and inter-annotator agreement
- Annotation difficulty and coverage statistics
- Label sets and class distributions
Common Workflows
Many datasets require processing raw telemetry and metadata before documentation:
- GPS Extraction: Extract coordinates from flight logs (SRT files, EXIF data, telemetry logs)
- Event Aggregation: Aggregate video-level data to mission/session-level Darwin Core events
- Occurrence Generation: Link species detections to biodiversity occurrence records
- Statistics Calculation: Compute coverage metrics, annotation counts, and class distributions
Worked Example
See the Kenyan Animal Behavior Recognition Dataset with Telemetry for an example dataset that is FAIR² Drones compliant. See also the KABR processing scripts for Python examples of GPS extraction, event aggregation, and Darwin Core generation.
Target Audiences
- Ecologists: Documenting wildlife surveys for biodiversity databases and research publications
- Computer Vision Researchers: Creating benchmark datasets for algorithm development
- Robotics Engineers: Developing autonomous systems and testing perception pipelines
- Conservation Practitioners: Sharing monitoring data across organizations
- Data Scientists: Training and evaluating machine learning models
Citation
If you use this standard or template, please cite:
@misc{fair_drone_standard,
title={FAIR² Drones Data Standard for Wildlife Datasets},
author={Jenna Kline, Elizabeth Campolongo},
year={2026},
publisher={GitHub},
howpublished={\\url{https://github.com/Imageomics/fair_drones}}
}
Contributing
We welcome contributions to improve and extend this standard:
- Report issues or unclear documentation via GitHub Issues
- Submit example dataset cards
- Propose extensions for additional domains or modalities
- Contribute validation tools and utilities
License
This standard and documentation are licensed under CC BY 4.0.
Acknowledgements
This work builds upon:
- FAIR Principles for scientific data management
- Darwin Core biodiversity data standards
- Hugging Face Dataset Cards for ML datasets
- Imageomics Dataset Card Template for biodiversity and computer vision dataset documentation
- UAV best practices from Barnas et al. 2020
Support
For questions, comments, or concerns:
- Open an issue on GitHub
- Refer to the Quick-Start Guide for implementation guidance
- Review example implementations for reference
| Project Status: Active development | Version 1.0 (2025) |