fair_drones

FAIR² Drone Dataset Card: Quick-Start Guide

For researchers creating or documenting drone-based wildlife datasets

Below is a streamlined checklist for creating a WildFAIRx compliant drone dataset card. Review the full dataset card template for detailed guidance.

Before You Start

• Gather image/video files and annotation files (if applicable)
• Collect drone/sensor specifications (model numbers)
• Locate flight logs or mission notes
• Find research permits and approval numbers
• Create species list with scientific names (use GBIF)
• Set aside 2-3 hours for completion

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Choose Your Template

Select the appropriate template based on your primary use case:

• Object Detection: Detection Template (~2 hours) - Core + Darwin Core + COCO annotations
• Multi-Object Tracking: Tracking Template (~2.5 hours) - Detection + MOT format + ID protocols
• Behavior Recognition: Behavior Template (~3 hours) - Detection + Ethogram + temporal labels
• Robotics Benchmarking: Platform Template (~2.5 hours) - Core + Full telemetry + Minimal annotations
• Multiple Tasks: Comprehensive Template (~3-4 hours) - All modules

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YAML Front Matter

• License: Specify license type (e.g., cc-by-4.0)
• Pretty Name: Provide descriptive dataset name
• Task Categories: List relevant tasks (e.g., object-detection, image-classification)
• Tags: Include relevant tags (e.g., wildlife-monitoring, species names, locations)
• Size Categories: Specify dataset size (e.g., n<1k, 1k<n<10k)

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Dataset Overview

• Title: Clear, descriptive title
• Description: 2-3 paragraph summary of dataset purpose and content
• Authors: List curators/authors with affiliations
• Contact Information: Provide contact details or point to discussion forum
• Repository: Link to GitHub repository
• Homepage: Provide link if available
• DOI: Assign or note as pending

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Data Structure

• Directory Structure: Document file organization with tree diagram
• File Formats: List formats for images (e.g., JPG, PNG) and annotations (e.g., COCO JSON, YOLO)
• Naming Convention: Explain file naming pattern with examples
• Data Splits: Describe train/val/test splits with counts and creation method
• Example Files: Link to at least one representative image

!!! warning “Consistent Naming Conventions”

Ensure file naming is consistent across videos, telemetry files, and occurrence data:
- Use the same date format throughout (e.g., `YYYY_MM_DD` or `DD_MM_YY`)
- Match session/flight identifiers exactly between CSV files and data folders
- Document any naming changes (e.g., `session_1` → `flight_1`) to maintain data linkages
- Test that scripts can locate files using your naming pattern

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Darwin Core Metadata

Event Records

For each survey location/date, document:

• eventID: Unique identifier per mission
• eventDate: Date in ISO format (YYYY-MM-DD)
• eventTime: Time with timezone
• decimalLatitude/Longitude: Coordinates in WGS84
• coordinateUncertaintyInMeters: GPS accuracy (typically 5-10m)
• locality: Study site description
• habitat: Habitat type
• samplingProtocol: Survey method (e.g., “UAV transect at 60m AGL”)
• sampleSizeValue/Unit: Coverage area and units
• samplingEffort: Duration or effort metric

!!! tip “Extracting GPS Data from Telemetry”

If your videos have embedded telemetry (SRT files, EXIF data, or flight logs), you can extract GPS coordinates programmatically:
- Use `exiftool` for EXIF GPS data from images
- Parse DJI `.SRT` files for frame-level GPS coordinates
- Extract launch points, min/max bounds, and altitude ranges
- Aggregate video-level data to session/mission-level events
- See [KABR scripts](https://github.com/Imageomics/kabr-behavior-telemetry/tree/main/scripts) for Python examples

Occurrence Records

• Scientific Names: Verify all species names with authority via GBIF
• Taxonomic Hierarchy: Complete Kingdom, Phylum, Class, Order, Family, Genus, Species for each taxon
• Occurrence Table: Create table linking events to species observations

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Platform Specifications

• Platform Type: Drone type (multirotor, fixed-wing, hybrid)
• Manufacturer and Model: Full platform identification
• Physical Specs: Weight, dimensions, flight time, max speed, wind resistance
• Sensor Details: Camera manufacturer, model, resolution, sensor size, focal length, field of view
• Additional Sensors: List any thermal, LiDAR, multispectral sensors
• Gimbal: Type and axes of stabilization
• Autonomy Mode: Manual, waypoint, or fully autonomous
• Flight Features Used: Grid, orbit, follow, terrain-following, etc.

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Mission Parameters

• Flight Altitude: Range in meters AGL
• Flight Speed: Speed in m/s
• Flight Pattern: Description (grid, transect, adaptive)
• Coverage: Area covered per mission
• Image Overlap: Forward/side overlap percentages
• Environmental Conditions: Weather, temperature, wind, visibility
• Telemetry Data: Available flight logs and formats (GPS, IMU, battery, etc.)
• Permits: Research permits, IRB/IACUC approvals, aviation regulations followed
• Animal Welfare: Minimum altitudes, disturbance protocols

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Annotations

Format and Coverage

• Supported Tasks: Detection, tracking, segmentation, behavior, re-ID, keypoints
• Annotation Format: Specify format for each task (COCO, MOT, etc.)
• Label Set: List all classes/species, behaviors, attributes
• Total Counts: Images, annotations, annotations per image (min/max/avg)
• Per-Class Distribution: Count per class/species

Quality Metrics

• Creation Method: Manual, semi-automatic, or automatic
• Annotation Tool: Software used (CVAT, Label Studio, etc.)
• Annotators: Who created annotations (experts, students, crowd workers)
• Quality Assurance: Number of annotators, inter-annotator agreement, review process
• Confidence Scores: Whether included in annotation files
• Known Issues: Annotation gaps, difficult cases, systematic biases

Annotation Difficulty

• Occlusion: Percentage of instances with none/partial/heavy occlusion
• Crowd Density: Distribution across sparse/moderate/dense scenarios
• Scale Variation: Range of object sizes
• Environmental Challenges: Glare, shadows, motion blur, etc.

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Dataset Statistics

• Temporal Coverage: Date range, seasons represented
• Spatial Coverage: Number of locations, total area surveyed
• Class Balance: Distribution across classes/species
• Baseline Results: Performance metrics if available

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Limitations and Considerations

• Known Biases: Geographic, temporal, species, environmental
• Limitations: Technical constraints, coverage gaps, quality issues
• Recommendations: Guidance for appropriate dataset use
• Ethical Considerations: Privacy, animal welfare, cultural sensitivities
• Reporting Issues: Link to issue tracker or community forum

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Licensing

• License Details: Confirm license choice and any special conditions
• Component Licenses: Note if images, annotations, or code have different licenses
• Attribution Requirements: Specify how to cite the dataset

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Citation

• Dataset Citation: Provide BibTeX entry for the dataset
• Associated Paper: Include citation for related publications

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Acknowledgements

• Funding Sources: List grants and funding agencies
• Contributors: Acknowledge field teams, annotators, collaborators
• Institutional Support: Note supporting organizations

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Optional Sections

• Glossary: Define technical terms or specialized calculations
• Additional Information: Any other relevant context
• Related Datasets: Links to complementary datasets
• Multimodal Linkages: Connections to other sensor data

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Validation Checklist

WildFAIRx Compliance

• DOI assigned or pending
• License clearly stated
• All required (*) fields completed in template
• Machine-readable YAML front matter filled
• Contact information provided

Darwin Core Compliance

• Event records complete (dates, locations, protocol)
• Occurrence records have scientific names with authorities
• Taxonomic hierarchy filled (minimum to family level)
• Sampling effort quantified
• Coordinates in WGS84 with uncertainty

Practical Completeness

• Directory structure documented
• File naming convention explained
• Data splits clearly defined
• Annotation format specified
• At least one example image linked
• Known limitations acknowledged

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Common Mistakes to Avoid

Vague Descriptions ❌ “We used a drone to collect wildlife images” ✓ “DJI Matrice 300 RTK with Zenmuse H20T camera flew grid patterns at 60m AGL”

Missing Geographic Precision ❌ “Collected in Tanzania” ✓ “Serengeti National Park, Mara Region (-2.3456, 34.8123 ±5m)”

Unclear Sampling Effort ❌ “Multiple flights” ✓ “45 missions totaling 30 flight hours, covering 2,500 hectares”

Incomplete Species Names ❌ “elephants, zebras, giraffes” ✓ “Loxodonta africana, Equus quagga, Giraffa camelopardalis”

Undocumented Splits ❌ “Split into train/val/test” ✓ “Stratified by location and season: missions 1-300 (train), 301-350 (val), 351-400 (test)”

Hidden Biases ❌ “Representative wildlife dataset” ✓ “Dry season only; large-bodied species overrepresented; morning flights bias against nocturnal species”

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Time-Saving Tips

Before Starting:

• Gather all information before opening the template
• Copy from existing paper methods sections
• Look up drone/camera specs online from manufacturer sites

While Completing:

• Start with easy sections (Overview, Structure) to build momentum
• Mark sections to revisit with TODO notes
• Use EXIF data from images for missing camera/GPS information
• Estimate reasonably if exact values unavailable (note as approximations)

Automation Opportunities:

• Write scripts to extract GPS/altitude from telemetry files (SRT, EXIF, flight logs)
• Generate occurrence records from detection annotations
• Calculate statistics (image counts, annotation distributions) programmatically
• Aggregate frame-level or video-level data to session-level events
• Validate Darwin Core format compliance automatically

For Missing Information:

• Document what’s unavailable rather than leaving blank
• Contact original research team if retrofitting dataset
• Use “not available” or “not recorded” explicitly

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Resources

Validation Tools

• Python scripts for template generation and validation (coming soon)
• Darwin Core export tools for GBIF submission
• HuggingFace conversion utilities

External Resources

• Darwin Core Quick Reference
• GBIF Species Search
• FAIR² Principles Paper
• UAV Best Practices (Barnas et al.)

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Getting Help

!!! question “Questions, Comments, or Concerns?”

For assistance:

• Report issues or unclear sections via GitHub Issues
• Contribute example cards or improvements
• Share feedback on the template

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Ready to start?

1. Download the appropriate template for your task
2. Gather your information using the checklist above
3. Set aside 2-3 hours to complete the card
4. Follow the template section by section
5. Validate your completed card
6. Publish your WildFAIRx compliant dataset!

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