This dataset contains multi-modal UAV-based wildlife observations collected in Ol Pejeta Conservancy, a privately managed conservation area in central Kenya. The primary target species is the African lion, observed within open savannah and bushland ecosystems.

The dataset includes synchronized RGB and thermal video recordings captured using a UAV platform during controlled experimental approaches. Drone flights were conducted at multiple fixed altitudes to evaluate both animal behavioral responses to UAV disturbance and to enable multi-scale visual wildlife monitoring.

The data were collected over a 15-day field campaign (October–December 2024) during daylight hours (08:00–19:00). Observations span multiple lion prides across heterogeneous savannah habitats dominated by acacia and mixed grassland vegetation.

This dataset was created to support research in wildlife monitoring, UAV-based ecological sensing, and animal behavior analysis, with a particular focus on understanding how large carnivores respond to aerial robotics in real-world conservation environments.

Supported Tasks and Applications

🤖 Computer Vision Tasks

Object Detection (bounding boxes around lions in RGB and thermal imagery)
Instance Segmentation (potential pixel-level labeling of individuals in future annotations)
Multi-Object Tracking (consistent identity tracking across video frames)
Re-identification (individual lion recognition)
Behavior Recognition (classification of responses to drone presence, social behaviors)
Cross-modal Learning (RGB–thermal fusion for robust detection under variable vegetation and lighting)

🌿 Ecological Applications

Population abundance estimation (group size estimation from aerial imagery)
Behavioral analysis (quantifying UAV-induced disturbance responses)
Habitat use patterns (space-use inference within savannah and bushland environments)
Species distribution modeling (within protected conservation areas)
Wildlife monitoring and conservation decision support
Human–wildlife interaction studies (response to aerial systems and anthropogenic noise)

🤖 Robotics Applications

UAV perception system benchmarking in real-world wildlife environments
Aerial robotics safety and disturbance minimization studies
Sensor fusion benchmarking (RGB + thermal + telemetry integration)

Dataset structure

Directory organization

lion-responses-to-aerial-monitorin-dataset/
└── dataset/
   ├── data/
   │   ├── AJ02/         # the first 2 digits are the initials of a pride name, followed by the number of the trial on that pride
   │   │   ├── DJI_20241107134254_0001_S.MP4   # 704.6 MB
   │   │   ├── DJI_20241107134254_0001_S.SRT
   │   │   ├── DJI_20241107134254_0001_T.MP4   # 144 MB
   │   │   ├── DJI_20241107134254_0001_T.SRT
   │   │   ├── DJI_20241107134254_0001_V.MP4   # 2.02 GB
   │   │   ├── DJI_20241107134254_0001_V.SRT
   │   ├── AJ03/  … (3 clips × {MP4, SRT})
   │   ├── AJ04/  … (3 clips × {MP4, SRT})
   │   ├── AJ05/  … (3 clips × {MP4, SRT})
   └── metadata/
       ├── darwin_core_occurrences.csv   # Darwin Core — one row per clip  
       └── darwin_core_events.csv        # Darwin Core — one row per mission

Data instances

Darwin Core Event Tables:

data/darwin_core_occurrences.csv — one row per video clip.

data/darwin_core_events.csv — one row for the full mission.

Raw Videos (data/raw/mission_1//.{MP4,SRT}):

Original 4K footage as recorded on-board, screen recording and Thermal footage with DJI sidecar files:

MP4 — H.264/H.265-encoded 4K (3840 × 2160) video at ~30 fps MP4 Thermal - 640 x 512 at ~30 fps SRT — DJI subtitle telemetry file; one entry per frame with GPS, altitude, camera settings, and UTC timestamp (source for the occurrence CSVs) File naming follows the DJI convention: DJI_YYYYMMDDHHMMSS_NNNN_D where NNNN is the clip index on the SD card.

Data Fields

Key field groups:

🌿 Darwin Core Event Fields

data/metadata/darwin_core_occurences.csv:

eventID
eventDate
eventTime
decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, geodeticDatum
locality, habitat
samplingProtocol, Altitude (mAGL)

data/metadata/darwin_core_events.csv:

occurrenceID
eventID
scientificName, kingdom, phylum, class, order, family, genus, species
taxonRank
Count_Individuals, Count_AdultFemales, Count_AdultMales, Count_cubs # counts are for eventID, not for occurrenceID
Behaviors

Platform and Mission Specifications

🚁 Platform Details

Type: UAV (Unmanned Aerial Vehicle)

Hardware:

Manufacturer: DJI Enterprise
Model: DJI Mavic 3 Enterprise T
Weight: 0.92 kg
Max flight time: 45 min
Max speed: 21 m/s
Wind resistance: 12 m/s

Autonomy:

Mode: Semi-autonomous
Navigation: Manual experimental approaches with hovering
Collision avoidance: Yes (omnidirectional obstacle sensing)
Return-to-home: Yes

Payload:

Integrated payload system (non-modular)
3-axis stabilized gimbal (tilt, roll, pan)

📷 Sensor Specifications

Primary Sensor: Integrated RGB + Thermal Imaging System

RGB Camera

Type: RGB
Manufacturer: DJI
Model: Mavic 3T Wide Camera
Resolution: 3840 × 2160 pixels
Sensor size: 1/2-inch CMOS
Focal length: 24 mm equivalent
Field of view: 84°
Frame rate: 4K at 30 fps
Bit depth: 8-bit JPEG/H.264 video

Thermal Camera

Type: Thermal
Manufacturer: DJI
Model: Mavic 3T Thermal Camera
Resolution: 640 × 512 pixels
Sensor type: Uncooled VOx Microbolometer
Frame rate: 30 Hz
Infrared wavelength: 8–14 μm
Focal length: 40 mm equivalent
Field of view: 61° DFOV

Spectral Bands

Band	Wavelength	Purpose
RGB Visible	~400–700 nm	Visual identification and behavioral monitoring; detection and counting of lions under varying vegetation/light conditions
Thermal Infrared	8–14 μm	Visual identification and behavioral monitoring; detection and counting of lions under varying vegetation/light conditions

Calibration

Calibrated: Factory calibrated
Method: Manufacturer calibration

Synchronization (multi-sensor)

Method: Integrated hardware synchronization within DJI imaging system

🗺️ Mission Parameters

Flight Specifications

Altitude: 20–120 m AGL
Speed: 3–10 m/s
Flight pattern: Approach followed by stationary hover or manual tracking
Coverage per mission: Opportunistic wildlife encounters (no fixed-area survey)
Image overlap: Not applicable
Ground sampling distance: Variable depending on altitude

Environmental Conditions

Temperature range: 12–28°C
Weather: Typical savannah conditions (clear to partly cloudy)
Time of day: 08:00–19:00
Season: Short rainy / transitional season (October–December)

🔍 Sampling Protocol

Survey Design

Opportunistic wildlife monitoring combined with controlled experimental drone approaches. Lions were located using GPS collars, VHF telemetry, and opportunistic sightings reported by field personnel and safari guides.

Flight Operations

Drone launched from open-roof field vehicles
Vertical takeoff to predetermined altitude
Fixed altitude range: 20–120 m AGL

Ethical procedures:

Flights stopped if flee response observed
Hover duration minimized
Compliance with Kenyan aviation regulations

Data Collection

15 field days (Oct–Dec 2024)
RGB + thermal video recording
Live aerial group counting
Behavioral response documentation
Opportunistic individual identification imagery

Quality Control

Real-time visual inspection of imagery
Cross-validation with ground-based counts
Identification review for individual markers
Supervised by experienced wildlife monitors

📋 Permits and Compliance

KCAA operational permission
WRTI Research Permit: WRTI-0431-06-24
NACOSTI License: NACOSTI/P/24/41404

Regulations followed:

Kenyan UAV regulations
Wildlife research permitting requirements
Conservancy operational protocols

Ethics Approval

Not required.

Animal Welfare Protocol

Altitude treatments used to evaluate disturbance thresholds
Flights modified or terminated under strong negative responses
No prolonged pursuit during flee behavior

Dataset Creation

Curation Rationale

This dataset was created to investigate drone-based RGB and thermal imagery for lion monitoring and behavioral response assessment.

Scientific Questions

How do lions respond to drones at different altitudes?
Can drones improve wildlife population monitoring accuracy?
Can thermal imaging improve detection in field conditions?

Gap Filled

Existing datasets rarely combine:

Experimental disturbance analysis
Thermal + RGB fusion
Large carnivore UAV monitoring
Ground-truth validation

Intended Use Cases

Wildlife detection and counting
Behavioral analysis
Thermal object detection
UAV disturbance studies
Conservation AI research

Source Data

Field Collection

Opportunistic sampling
GPS collar and VHF telemetry assisted targeting
Manual drone deployment from vehicles

Post-Processing

Filtering usable imagery/video
Cross-referencing aerial and ground counts

Tools Used

DJI Pilot 2

Data Producers

Elena Iannino (pilot)
Simon Irungu, Ol Pejeta Conservancy staff (assistant pilot)
Kelvin Mutethia, Ol Pejeta Conservancy staff (ground observer)

Annotations

This dataset contains raw telemetry data only.

No:

bounding boxes
tracking IDs
behavior labels

Annotation tools such as CVAT may be used by researchers.

Personal and Sensitive Information

Human subjects: minimal exposure, managed conservancy operations
Wildlife: African lion (Panthera leo, Vulnerable - IUCN 2025)
Location: Ol Pejeta Conservancy (controlled access area)
Full GPS included for reproducibility
No security concerns

Dataset Statistics

Property	Value
Session date	Oct–Dec 2024 (15 field days)
Location	Ol Pejeta Conservancy
GPS location	~0°00' N, 36°54' E
Elevation	~1,810 m ASL
Species	African lion
Drone	DJI Mavic 3 Enterprise T
Altitudes	20, 40, 60, 80, 100, 120 m
Flight mode	Manual experimental approach
Speed	3–10 m/s
Time	08:00–19:00
Sensors	RGB + Thermal
Telemetry	DJI onboard GPS
Sampling	Opportunistic + experimental

Bias, Risks, and Limitations

Geographic Bias

Only savannah ecosystem (Kenya).

Temporal Bias

Daytime only, Oct–Dec 2024.

Species Bias

Only African lions.

Detection Bias

vegetation occlusion
altitude-dependent visibility
thermal variability

Technical Limitations

thermal lower resolution than RGB
motion blur in tracking
variable framing

Recommendations

For Detection Models

multi-scale augmentation
altitude-aware training
RGB/thermal separate pipelines

For Ecological Analysis

detection probability correction
habitat visibility modeling
avoid cross-ecosystem generalization

For Transfer Learning

fine-tune across altitudes
domain adaptation required
fusion improves robustness

What This Dataset Should NOT Be Used For

absolute population estimates without correction
nocturnal ecology inference
cross-ecosystem generalization
unethical wildlife tracking
long-term behavioral inference

Licensing Information

Dataset License: CC BY 4.0
Code License: MIT

Citation

Dataset:

@misc{lion_responses_to_aerial_monitoring_dataset2024,
  author = {Elena Iannino},
  title = {Behavioral Ecology: Lion Responses to Aerial Monitoring in Ol Pejeta Conservancy},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Eiannino/lion-responses-to-aerial-monitoring-dataset}
}

Associated Paper:

@article{eiannino,
  title  = {Drone-based aerial monitoring improves demographic accuracy of lion groups with low behavioral impact},
  author = {Iannino, Elena and others},
  year   = {2026},
  note   = {In preparation}
}

FAIR² Drone Data Standard:

@article{kline2025fair2,
  title  = {Toward a FAIR² Standard for Drone-Based Wildlife Monitoring Datasets},
  author = {Kline, Jenna and others},
  year   = {2025},
  note   = {In preparation}
}

Acknowledgements

We thank: • Ol Pejeta Conservancy management and wildlife monitoring teams • Licensed UAV pilots and field researchers • Kenyan regulatory agencies for permitting support • Wildlife Research and Training Institute (WRTI) • National Commission for Science, Technology and Innovation (NACOSTI) • Data collection team: Simon Irungu and Kelvin Mutethia

This work is supported by the WildDrone MSCA Doctoral Network funded by EU Horizon Europe under grant agreement no. 101071224, and by the Innovation Fund Denmark for the project DIREC (9142-00001B).

Validation and Quality Metrics

🤖 AI-Readiness Validation

• Machine-readable metadata: Yes

🌿 Darwin Core Validation

Event records documented
Coordinates recorded in UTM
Sampling protocol described
Scientific naming follows accepted taxonomy (Panthera leo)

FAIR² Compliance Checklist

Principle	Status
Findable	Pending DOI and registry indexing
Accessible	Intended open-access release
Interoperable	Standard metadata structure used
Reusable	Licensing and provenance partially documented
AI-Ready	Multi-modal structured imagery suitable for ML workflows

Glossary

AGL: Above Ground Level
Darwin Core: Biodiversity metadata standard maintained by TDWG
FAIR²: FAIR principles extended for AI-ready datasets
GSD: Ground Sampling Distance
AGL: Above ground level altitude
UAV: Unmanned Aerial Vehicle
TDWG: Biodiversity Information Standards organization

Dataset Card Authors

Elena Iannino

Dataset Card Contact

Primary Contact: Elena Iannino
GitHub: https://github.com/Eiannino
Hugging Face: https://huggingface.co/datasets/EIannino/lion-responses-to-aerial-monitoring-dataset

Version History

v1.0.0 (2026-05-12): Initial release

Notes

This dataset card follows the FAIR² Drone Data Standard (Kline et al., 2025) and is modeled on the KABR Behavior Telemetry dataset card.

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