edouard-rolland/multi-perspective-dataset-plain-zebras

Dataset Card for Multi-Perspective Dataset of Plains Zebras

Synchronised per-frame telemetry from four simultaneously operating drones, enabling research on multi-view wildlife monitoring, individual re-identification, 3D reconstruction, and multi-drone swarm survey protocols.

Dataset Details

This dataset provides per-frame telemetry from a coordinated swarm of four simultaneously operating DJI Mini 4 Pro drones monitoring plains zebras (Equus quagga) at Ol Pejeta Conservancy, Laikipia County, Kenya. Collected on 24 February 2026, the dataset contains 11 synchronised video clips (86,985 frames) with complete GPS tracks, camera parameters, and full decoded DJI flight-controller logs.

The dataset was developed to demonstrate the FAIR² Drones standard for coordinated multi-platform wildlife surveys. Each drone was assigned a fixed altitude (30, 45, 60, or 75 m AGL) to maximise complementary coverage: vertical monitoring for census and movement analysis, horizontal monitoring for individual identification via flank markings. No annotation labels are included — this is a raw multi-perspective telemetry dataset intended for synchronised multi-view wildlife monitoring research.

Key features:

• 4-drone synchronised session covering 723 seconds of simultaneous recording
• 11 per-drone video clips with per-frame GPS, altitude, and camera EXIF
• 62 flight-controller columns per frame (attitude, velocity, battery, gimbal, RC signal)
• Cross-drone frame alignment table with 21,747 rows at 100 ms tolerance
• Darwin Core event tables: 11 video-level events + 1 session-level event
• Full processing pipeline released as open-source Python/Node.js scripts
• No annotation labels — raw multi-perspective telemetry for geometry and monitoring research

Supported Tasks and Applications

This dataset supports computer vision, ecological analysis, and autonomous systems research:

🤖 Computer Vision Tasks:

• Individual Re-identification (exploiting multi-perspective flank markings across the four drones)
• Multi-Object Tracking (temporal consistency within and across drone views)
• Object Detection (bounding box baselines at varying altitudes 30–75 m)
• 3D Pose Estimation and Reconstruction (multi-view geometry from field-recorded extrinsics)

🌿 Ecological Applications:

• Group size and movement estimation from simultaneous multi-altitude aerial views
• Altitude-dependent detection performance characterisation
• Animal response to drone presence
• Synchronised multi-perspective behavioural context reconstruction

🚁 Drone Systems Research:

• Multi-drone swarm synchronisation methods and evaluation
• Cross-platform temporal alignment validation

Dataset Structure

Directory Organisation

multi-perspective-dataset-plain-zebras/
├── data/
│   ├── raw/
│   │   └── mission_1/                     # original 4K clips with DJI sidecar files
│   │       ├── drone_1/
│   │       │   ├── DJI_20260224133555_0001_D.MP4   # 3.6 GB
│   │       │   ├── DJI_20260224133555_0001_D.SRT
│   │       │   ├── DJI_20260224133555_0001_D.LRF
│   │       │   ├── DJI_20260224134121_0002_D.MP4   # 3.6 GB
│   │       │   ├── DJI_20260224134121_0002_D.SRT
│   │       │   ├── DJI_20260224134121_0002_D.LRF
│   │       │   ├── DJI_20260224134647_0003_D.MP4   # 815 MB
│   │       │   ├── DJI_20260224134647_0003_D.SRT
│   │       │   └── DJI_20260224134647_0003_D.LRF
│   │       ├── drone_2/  … (3 clips × {MP4, SRT, LRF})
│   │       ├── drone_3/  … (2 clips × {MP4, SRT, LRF})
│   │       └── drone_4/  … (3 clips × {MP4, SRT, LRF})
│   ├── occurrences/
│   │   └── mission_1/                     # per-frame telemetry CSVs (11 files, 86,985 rows)
│   │       ├── drone_1-DJI_20260224133555_0001_D.csv   # 9,761 rows
│   │       ├── drone_1-DJI_20260224134121_0002_D.csv   # 9,770 rows
│   │       ├── drone_1-DJI_20260224134647_0003_D.csv   # 2,216 rows
│   │       ├── drone_2-DJI_20260224133554_0002_D.csv   # 9,757 rows
│   │       ├── drone_2-DJI_20260224134119_0003_D.csv   # 9,762 rows
│   │       ├── drone_2-DJI_20260224134645_0004_D.csv   # 2,231 rows
│   │       ├── drone_3-DJI_20260224133556_0001_D.csv   # 12,465 rows
│   │       ├── drone_3-DJI_20260224134252_0002_D.csv   # 9,274 rows
│   │       ├── drone_4-DJI_20260224133554_0001_D.csv   # 9,759 rows
│   │       ├── drone_4-DJI_20260224134120_0002_D.csv   # 9,762 rows
│   │       └── drone_4-DJI_20260224134646_0003_D.csv   # 2,228 rows
│   ├── sync/
│   │   └── mission_1/
│   │       └── synchronized_frames.csv    # full cross-drone sync table (21,747 rows × 93 cols)
│   ├── trimmed/
│   │   └── mission_1/
│   │       ├── drone_1.mp4                # trimmed 4K video — common window
│   │       ├── drone_2.mp4
│   │       ├── drone_3.mp4
│   │       ├── drone_4.mp4
│   │       └── synchronized_trim.csv      # trim-frame index table (18,292 rows)
│   ├── video_events.csv                   # Darwin Core — one row per clip (11 rows)
│   └── session_events.csv                 # Darwin Core — one row per mission (1 row)
├── flight_logs/
│   └── mission_1/
│       └── raw.csv                        # decoded DJI v14 flight logs (34,954 rows × 62 cols)
└── scripts/                               # full processing pipeline

Data Instances

Occurrence Files (data/occurrences/mission_1/<drone_id>-<video_id>.csv):

Each CSV (84 columns) contains frame-by-frame records for one continuous video clip from one drone. The 11 files together cover 86,985 rows.

SRT-derived columns (per frame, from DJI subtitle telemetry):

Field	Example Value	Description
occurrenceID	mission_1_drone_1_DJI_20260224133555_0001_D_1	Unique occurrence identifier (<mission>_<drone>_<video>_<frame>)
eventID	multi-perspective-dataset-plain-zebras_mission_1_drone_1_…	Darwin Core event identifier
mission	mission_1	Mission label within the session
drone_id	drone_1	Drone identifier (drone_1 – drone_4)
video_id	DJI_20260224133555_0001_D	DJI filename stem
frame	1	1-based frame index within the clip
srt_timecode	00:00:00,000	SRT timecode (HH:MM:SS,mmm)
date_time	2026-02-24 13:35:56.003	Frame UTC timestamp (YYYY-MM-DD HH:MM:SS.mmm)
sync_utc_ms	1771936556003	Unix epoch milliseconds (UTC) — primary sync key
latitude	-0.007247	Aircraft decimal latitude (WGS84, from SRT)
longitude	36.873998	Aircraft decimal longitude (WGS84, from SRT)
rel_alt	29.7	Altitude above takeoff point (m)
abs_alt	1941.856	Altitude above sea level (m)
iso	110	Camera ISO
shutter	1/2500.0	Shutter speed
fnum	1.7	Aperture f-number
ev	0	Exposure value
color_md	default	Colour mode
focal_len	24.0	Focal length (mm, 35 mm equivalent)
ct	5517	Colour temperature (K)
video_file	missions/mission_1/drones_videos/drone_1/DJI_…MP4	Relative path to source MP4
scientificName	Equus quagga	Darwin Core scientific name
kingdom	Animalia	Darwin Core kingdom
taxonRank	species	Darwin Core taxon rank

Flight-log-derived columns (nearest-neighbour joined from raw flight log, ≤500 ms tolerance — 60 columns):

Field	Example Value	Description
fl_latitude / fl_longitude	-0.007244 / 36.873994	Aircraft GPS from flight log (cross-check)
fl_altitude	1913.256	GPS altitude ASL from flight log (m)
fl_height	1.1	Barometric height AGL (m)
heightMax	31.5	Maximum height reached in flight (m)
vpsHeight	0.0	Vision Positioning System height (m, valid < 10 m)
xSpeed / ySpeed / zSpeed	0.0 / 0.0 / 0.0	Body-frame velocity (m/s)
xSpeedMax / ySpeedMax / zSpeedMax	2.1 / 1.8 / 0.5	Maximum recorded velocities (m/s)
pitch / roll / yaw	-0.9 / 1.7 / -3.8	Aircraft attitude (degrees)
flyTime	10.0	Seconds since takeoff
flycState	GPSAtti	Flight controller state
flycCommand	—	Flight controller command
flightAction	—	Current flight action
goHomeStatus	—	Return-to-home status
isGpsUsed	True	Whether GPS fix is active
gpsNum	32	Number of GPS satellites tracked
gpsLevel	5	GPS signal level (0–5)
droneType	—	Aircraft model code
batteryPercent	92	Battery charge level (%)
voltage	16.4	Battery pack voltage (V)
batteryCurrent	2.1	Current draw (A)
batteryCellVoltages	4.11;4.11;4.11;4.11	Per-cell voltages (semicolon-separated, V)
batteryTemp	28.0	Battery temperature (°C)
gimbalMode	YawFollow	Gimbal mode
gimbalPitch / gimbalRoll / gimbalYaw	0.0 / 0.0 / 0.0	Gimbal attitude (degrees)
rcUplinkSignal / rcDownlinkSignal	90.0 / 92.0	RC link signal strength (%)
rcAileron / rcElevator / rcThrottle / rcRudder	1024	RC stick positions (PWM, 1024 = centre)
isPhoto / isVideo	False / True	Camera photo/recording state
homeLat / homeLon / homeAlt	-0.007257 / 36.873914 / 1941.8	Home-point GPS coordinates
homeHeightLimit	120.0	Maximum altitude limit (m)
homeGoHomeHeight	30.0	Return-to-home altitude (m)

Note: Flight-log columns are NaN for frames that fall outside the flight-log recording window (e.g. before arm or after video stop). All frames within the active flying window are matched at 100% in this dataset.

Naming Convention:

{drone_id}-{video_id}.csv
Example: drone_1-DJI_20260224133555_0001_D.csv
         └drone─┘ └──────────video_id──────────┘

Temporal Information:

• Date: 2026-02-24 (single-day survey)
• Session start: 13:35:54 UTC+1 / 12:35:54 UTC
• Session end: 13:48:01 UTC+1 / 12:48:01 UTC (727.2 s total)
• Common trimmed recording window: 609.7 s (~10 min, 18,292 frames per drone)
• Dry season, Laikipia County, Kenya

Synchronisation Table (data/sync/mission_1/synchronized_frames.csv, 21,747 rows × 93 cols):

Full cross-drone frame alignment using all four drones as co-anchored sources. Each row represents a ~33 ms tick. Columns follow the pattern {drone_id}_{field} for all SRT fields from each drone, plus sync_utc_ms as the join key.

Trimmed Synchronisation Table (data/trimmed/mission_1/synchronized_trim.csv, 18,292 rows × 14 cols):

Compact trim-frame index table for the common 4-drone recording window. Columns: trim_frame, sync_utc_ms, and {drone_id}_video_id, {drone_id}_frame, {drone_id}_srt_timecode for each of the four drones. The trim_frame index directly addresses frames in the corresponding drone_N.mp4 trimmed videos.

Darwin Core Event Tables:

data/video_events.csv (11 rows × 32 cols) — one row per video clip. Key columns: eventID, parentEventID, eventDate, eventTime, endTime, eventDurationSeconds, decimalLatitude, decimalLongitude, footprintWKT, samplingProtocol, samplingEffort, dynamicProperties (JSON with droneId, aircraftModel, mean AGL height, battery state, GPS quality, sync method).

data/session_events.csv (1 row × 51 cols) — one row for the full mission. Extends the video-event columns with Humboldt Eco fields: eco:inventoryTypes, eco:protocolNames, eco:protocolDescriptions, eco:targetTaxonomicScope, eco:samplingPerformedBy, eco:siteCount, etc.

Raw Videos (data/raw/mission_1/<drone_id>/<video_id>.{MP4,SRT,LRF}):

Original 4K footage as recorded on-board, with DJI sidecar files:

• MP4 — H.264/H.265-encoded 4K (3840 × 2160) video 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)
• LRF — DJI low-resolution proxy file (~480p); useful for fast preview and frame-level browsing without decoding the full 4K stream

File naming follows the DJI convention: DJI_YYYYMMDDHHMMSS_NNNN_D where NNNN is the clip index on the SD card.

Raw Flight Log (flight_logs/mission_1/raw.csv, 34,954 rows × 62 cols):

All four drone flight logs concatenated. One row per ~100 ms flight-controller tick. logFile column identifies the source drone. Contains all 60 flight-log columns described above, plus logFile and dateTime (ISO 8601 UTC string).

Data Fields

Key field groups:

🌿 Darwin Core Event Fields (data/video_events.csv, data/session_events.csv):

• eventID, parentEventID, eventDate, eventTime, endTime, eventDurationSeconds
• decimalLatitude, decimalLongitude, footprintWKT (bounding polygon in WKT)
• samplingProtocol, samplingEffort, locationID, countryCode, habitat
• dynamicProperties JSON with per-drone metadata (drone ID, aircraft model, mean AGL height, battery start/end %, mean GPS satellite count, sync method)
• Session events additionally include Humboldt Eco extensions (eco:inventoryTypes, eco:protocolNames, eco:targetTaxonomicScope, eco:samplingPerformedBy, etc.)

📍 Geolocation (occurrence files):

• latitude / longitude (WGS84, from SRT) — primary per-frame GPS
• fl_latitude / fl_longitude / fl_altitude (from flight log, cross-check)
• rel_alt — relative altitude above takeoff point (m)
• abs_alt — absolute altitude above sea level (m)
• fl_height — barometric height AGL (m)
• homeLat / homeLon / homeAlt — home-point GPS

📷 Camera Metadata (occurrence files):

• iso, shutter, fnum, ev, color_md, focal_len, ct
• All derived from the DJI SRT frame metadata block

✈️ Flight Dynamics (occurrence files, flight-log-derived):

• xSpeed, ySpeed, zSpeed and their maximums — body-frame velocity (m/s)
• pitch, roll, yaw — aircraft attitude (degrees)
• gimbalPitch, gimbalRoll, gimbalYaw and limit flags
• gpsNum, gpsLevel — GPS quality indicators
• flycState, flycCommand, flightAction — flight controller state machine
• flyTime — seconds since takeoff

🔋 Battery and Systems (occurrence files, flight-log-derived):

• batteryPercent, voltage, batteryCurrent, batteryCurrentCapacity, batteryFullCapacity
• batteryCellNum, batteryCellVoltages, batteryCellVoltageDev, batteryTemp, batteryTempMin, batteryTempMax
• rcUplinkSignal, rcDownlinkSignal, rcAileron, rcElevator, rcThrottle, rcRudder
• isPhoto, isVideo, sdCardInserted, sdCardState
• homeHeightLimit, homeGoHomeHeight

🔀 Synchronisation (data/sync/, data/trimmed/):

• sync_utc_ms — universal UTC epoch milliseconds join key, present in all files
• synchronized_frames.csv — full cross-drone correspondence (21,747 rows, 93 cols, all SRT fields per drone)
• synchronized_trim.csv — compact trim-frame index (18,292 rows, 14 cols) directly indexing the trimmed MP4s

Data Splits

This dataset has no pre-defined train/val/test splits.

Platform and Mission Specifications

🚁 Platform Details

Type: UAV (Unmanned Aerial Vehicle)

Hardware:

• Platform: DJI Mini 4 Pro
• Max flight time: ~34 minutes
• Wind resistance: Beaufort 5 (up to ~10 m/s)
• Number of platforms: 4 (simultaneous coordinated operation)

Autonomy:

• Mode: Autonomous flight with GPS stabilisation
• Navigation: Multi-drone coordination based on groundstation control
• Collision avoidance: Obstacle detection enabled
• Return-to-home: Automatic on signal loss

📷 Sensor Specifications

Primary Sensor: DJI Integrated 1/1.3″ CMOS Camera

• Type: RGB
• Resolution: 3840 × 2160 pixels (4K)
• Frame rate: ~30 fps (nominal)
• Bit depth: 8-bit
• Format: MP4 video (H.264/H.265)

Telemetry Included:

• GPS coordinates per frame (DJI SRT sidecar files, ~33 ms intervals)
• Camera settings (ISO, shutter, aperture, focal length, exposure value, colour temperature)
• Full flight-controller log (62 columns, ~100 ms ticks)
• UTC millisecond timestamp for cross-drone synchronisation (sync_utc_ms)

🗺️ Mission Parameters

Flight Specifications:

• Altitudes: 30, 45, 60, and 75 m AGL (one fixed altitude per drone)
• Speed: 0–10 m/s
• Flight pattern: Autonomous flight following the algorithm described in the associated paper (Rolland et al., 2025)
• Common recording window: 723 s (~12 min)
• Total clips: 11 (across 4 drones; some drones have more clips due to SD card splits)

Environmental Conditions:

• Season: Dry season (February)
• Weather: Cloudy
• Location: Ol Pejeta Conservancy, Laikipia County, Kenya
• Elevation: ~1,941 m ASL
• Habitat: African savanna / open grassland
• Time of day: Daytime

🔍 Sampling Protocol

Survey Design:

• Coordinated four-drone swarm; each drone assigned a different altitude for complementary multi-perspective coverage
• Focal group follow: 2 single plains zebra herd tracked continuously during the session
• Continuous video recording at 4K/~30 fps

Flight Operations:

• Licensed drone operators with Kenya Civil Aviation Authority approval
• Four licensed drone operators supervising the autonomous flight of the 4 drones
• Animals monitored for disturbance response

Data Collection:

• GPS telemetry embedded in DJI SRT sidecar files (one file per video clip)
• DJI binary flight logs (v14) decoded via DJI Open Platform API

Quality Control:

• Field notes recorded for the session
• Cross-drone frame match rate: 100% within 100 ms tolerance
• Per-clip visual inspection of video quality

Dataset Creation

Curation Rationale

This dataset was created to address two key research questions:

1. How can coordinated drone swarms provide complementary multi-perspective coverage of wildlife? By assigning each drone a fixed altitude (30–75 m AGL), the dataset simultaneously captures a census bird's-eye viewpoint and closer identification viewpoints, enabling analysis of the trade-offs between altitude, resolution, and field of view.

2. What are the technical requirements for temporal and spatial synchronisation across independent drone platforms? The pipeline documents and resolves the challenges of cross-drone clock alignment, SRT timezone ambiguity, and DJI v14 flight-log encryption, providing a fully reproducible open-source workflow.

The dataset fills a critical gap: most drone wildlife datasets contain single-perspective video; multi-drone synchronised datasets with complete flight-controller telemetry and open processing pipelines are rare. This dataset and its pipeline serve as the primary case study for the FAIR² Drones standard.

Source Data

Data Collection and Processing

Field Collection:

1. Planning:

   • Site selected based on known plains zebra population in the open savanna of Ol Pejeta Conservancy
   • Four DJI Mini 4 Pro drones assigned altitudes of 30, 45, 60, and 75 m AGL
   • Operators briefed
   • Flights conducted during daylight hours

2. Collection:

   • Operators located a focal zebra group
   • Drones launched and ascended to assigned altitudes
   • Simultaneous recording triggered by the groundstation
   • Continuous 4K video and SRT telemetry recorded during the session
   • DJI binary flight logs recorded automatically on-board and transferred post-flight

3. Post-Processing:

   • SRT files parsed to per-frame occurrence CSVs with UTC millisecond timestamps
   • DJI v14 binary flight logs decrypted via the DJI Open Platform API and decoded with dji-log-parser-js
   • Cross-drone frame alignment on sync_utc_ms using nearest-neighbour matching
   • Occurrence CSVs enriched with 62 flight-log columns
   • Video clips trimmed and concatenated to the common recording window using ffmpeg
   • Darwin Core event tables generated
   • HuggingFace-ready dataset assembled

Software and Tools Used:

• Flight control: DJI RC-N1 controller + DJI Fly app 5.17.0 (Android)
• Video capture: DJI Mini 4 Pro onboard recording
• DJI binary log decoder: dji-log-parser-js + custom Node.js script
• DJI Open Platform API: keychain decryption for v14 logs
• Telemetry parsing and enrichment: Python (pandas, numpy)
• Darwin Core event builder: custom Python scripts (this repository)
• Video trimming: ffmpeg

Annotations

This is a raw telemetry dataset with no animal detection boxes, track identities, or behaviour labels. All telemetry fields (GPS, camera settings, attitude, battery, gimbal) are automatically derived from on-board sensors and require no manual annotation.

The synchronisation table (data/sync/) links frames across drone views but does not include any manual labels.

Researchers wishing to add annotations (detection boxes, identities, behaviours) can use tools such as CVAT and align annotations to frames via sync_utc_ms.

Personal and Sensitive Information

Privacy and Security Considerations:

Human Subjects:

• Flights conducted in a managed conservancy away from public areas

Wildlife and Location:

• Target species Equus quagga (plains zebra) is not endangered (IUCN: Least Concern)
• Location corresponds to a well-managed, access-controlled conservancy (Ol Pejeta)
• Full GPS coordinates included to support scientific replication

Security:

• No security concerns
• Data collected in coordination with Ol Pejeta Conservancy management

Considerations for Using the Data

Dataset Statistics

Survey Summary:

Property	Value
Session date	2026-02-24
Location	Ol Pejeta Conservancy, Laikipia, Kenya
GPS bounding box	−0.0071 to −0.0058 N, 36.8705 to 36.8737 E
Elevation (ASL)	~1,942 m (min 1,941.86 m, max 1,980.55 m)
Target species	Plains zebra (Equus quagga)
Aircraft	DJI Mini 4 Pro × 4 (simultaneous)
Drone altitudes (AGL)	30, 45, 60, 75 m (one per drone)
Full session duration	727.2 s (~12 min)
Common trimmed window	609.7 s (~10 min)
Video clips	11
Raw video files	11 MP4 (4K, ~38 GB total) + 11 SRT + 11 LRF
Total telemetry frames	86,985
Cross-drone sync rows (full)	21,747
Trimmed sync rows	18,292
Raw flight-log rows	34,954
Frame rate	~30 fps
Synchronisation method	Per-frame UTC from SRT, cross-validated against isVideo edge (offset < 1.3 s)

Bias, Risks, and Limitations

⚠️ Known Biases:

1. Geographic Bias:

   • Data from a single site (Ol Pejeta Conservancy, Laikipia)
   • May not generalise to other savanna ecosystems or terrain types

2. Temporal Bias:

   • Single-day survey (2026-02-24, dry season)
   • No seasonal variation or multi-day coverage
   • Daytime flights only; nocturnal behaviour not captured

3. Species Bias:

   • Single species (Equus quagga, plains zebra)
   • Only the two herds are consistently in frame; background animals may be partially visible

4. Environmental Bias:

   • Dry season conditions; vegetation cover may differ from wet season
   • Open grassland terrain; performance in denser vegetation is untested

Technical Limitations:

• No annotations: No detection boxes, track identities, or behaviour labels are included
• GPS accuracy: ±5 m typical
• SRT timezone assumption: Processing assumes the device clock was set to UTC+1 (CET); different field timezones require reconfiguration in config.yaml
• Flight-log encryption: DJI v14+ logs require API decryption; API availability is subject to DJI policy changes (mitigated by cached .keychain.json sidecar files)
• Clip boundaries: Each drone records multiple sequential clips; raw clip indices are not globally continuous across clips (handled by the pipeline)

Recommendations

Best Practices for Using This Dataset:

1. For Multi-View Geometry / 3D Reconstruction:

   • Use sync_utc_ms as the universal join key; do not rely on raw per-clip frame indices across drones
   • Gimbal angles (gimbalPitch, gimbalRoll, gimbalYaw) provide orientation; combine with GPS for camera extrinsics

2. For Individual Re-identification:

   • Lower-altitude drones (30, 45 m) provide higher-resolution flank markings
   • Higher-altitude drones (60, 75 m) offer wider field of view for group-level context

3. For Ecological Analysis:

   • Dataset represents a single dry-season session; do not extrapolate to year-round or cross-site statistics without additional data

4. For Reproducing the Pipeline:

   • Set the processing machine to UTC+1 or configure the timezone offset in config.yaml
   • Cache .keychain.json sidecar files under version control to avoid API dependency

What This Dataset Should NOT Be Used For:

• Estimating absolute population sizes (non-systematic, single-session sampling)
• Generalising behaviour or detection performance to other sites, seasons, or species without additional validation

Licensing Information

Dataset License: CC BY 4.0 (Creative Commons Attribution 4.0 International)

Citation Requirement: Please cite the dataset and the associated paper if you use this data (see Citation section).

Code License: MIT License for scripts in this repository

Citation

If you use this dataset, please cite:

Associated Paper:

@InProceedings{10.1007/978-3-032-07638-0_22,
  author    = {Rolland, Edouard G. A. and Meier, Kilian and Gr{\o}ntved, Kasper A. R.
               and Laporte-Devylder, Lucie and Maalouf, Guy
               and Lundquist, Ulrik P. S. and Christensen, Anders L.},
  editor    = {Mathieu, Philippe and De la Prieta, Fernando},
  title     = {Drone Swarms for Multi-perspective Monitoring of Large Mammals
               in their Natural Habitats: Deployment and Field Trials},
  booktitle = {Advances in Practical Applications of Agents, Multi-Agent Systems,
               and Computational Social Science: The PAAMS Collection},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {266--277},
  isbn      = {978-3-032-07638-0}
}

Dataset:

@dataset{multi_perspective_zebras_2026,
  author  = {Rolland, Edouard and Afridi, Saadia and Bullock, Steve
             and {Jarabo Penas}, Alejandro and {Laporte Devylder}, Lucie},
  title   = {Multi-Perspective Dataset of Plains Zebras},
  year    = {2026},
  url     = {https://huggingface.co/datasets/edouard-rolland/multi-perspective-dataset-plain-zebras},
  license = {CC-BY-4.0},
  note    = {Ol Pejeta Conservancy, Kenya. 4 × DJI Mini 4 Pro, synchronised multi-drone aerial survey.}
}

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

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).

We thank:

• Ol Pejeta Conservancy for site access and logistical support
• Data Collection Team:
  • Saadia Afridi
  • Steve Bullock
  • Alejandro Jarabo Penas
  • Lucie Laporte Devylder
  • Elzbieta Pastucha

Validation and Quality Metrics

🤖 AI-Readiness Validation:

• Machine-readable metadata (YAML front matter complete)
• Structured telemetry in Darwin Core format
• Train/val/test splits pre-defined (users should create)
• Data loading code provided (Python pipeline scripts)
• Example notebooks (planned)

🌿 Darwin Core Validation:

• Event records complete and valid (11 video events + 1 session event)
• Occurrence records complete and valid (86,985 frames across 11 clips)
• Scientific names validated against GBIF backbone
• Coordinates in WGS84
• Sampling protocol documented
• GBIF dataset registration (planned)

⚠️ FAIR² Compliance Checklist:

• Findable: DOI to be assigned
• Accessible: Open access via HuggingFace (CC-BY-4.0)
• Interoperable: Darwin Core, WGS84, ISO 8601, CSV/JSON formats
• Reusable: CC-BY-4.0 license, full provenance and pipeline documented
• AI-Ready: Machine-readable, structured, versioned

Code and Tools

Data Loading (Python):

import pandas as pd

# Load session-level events
sessions = pd.read_csv('data/session_events.csv')

# Load video-level events
videos = pd.read_csv('data/video_events.csv')

# Load occurrence records for a specific drone clip
occurrences = pd.read_csv(
    'data/occurrences/mission_1/drone_1-DJI_20260224133555_0001_D.csv'
)

# Load the full cross-drone synchronisation table
sync = pd.read_csv('data/sync/mission_1/synchronized_frames.csv')

# Load the compact trim-frame index (references trimmed MP4s directly)
sync_trim = pd.read_csv('data/trimmed/mission_1/synchronized_trim.csv')

# Load the raw flight logs
flight_logs = pd.read_csv('flight_logs/mission_1/raw.csv')

# Get all four drone frames aligned to a given moment
sync_key = occurrences.loc[occurrences['frame'] == 100, 'sync_utc_ms'].values[0]
aligned = sync[sync['sync_utc_ms'].between(sync_key - 50, sync_key + 50)]

Processing Scripts:

See pipeline/ for:

• parse_srt.py — Parse DJI SRT files into per-frame occurrence CSVs with UTC timestamps
• sync.py — Cross-drone frame alignment on sync_utc_ms using nearest-neighbour matching
• trim.py — Trim and concatenate video clips to the common recording window via ffmpeg
• flight_logs.py — Decrypt and decode DJI v14 binary flight logs
• enrich.py — Join 62 flight-log columns to occurrence CSVs
• events.py — Generate Darwin Core video and session event tables
• assemble.py — Assemble the HuggingFace-ready dataset directory

Run everything with:

python run_pipeline.py

See the Quickstart section for full usage instructions.

---

Quickstart

Requirements

• Python 3.10+
• Node.js 12+ (for DJI flight log decoding)
• A DJI Open Platform API key — free at https://developer.dji.com/flight_logs/ (only needed for v13+ encrypted logs; skip if .keychain.json sidecars are already present)

Installation

git clone <repo-url>
cd fair_drone_data_standard
python -m venv .venv && source .venv/bin/activate
pip install pandas numpy requests pyyaml
npm install          # installs dji-log-parser-js and node-fetch

Add your data

Drop your mission folders into missions/ following this layout:

missions/
  mission_1/
    drones_videos/
      drone_1/   ← DJIFlightRecord .SRT / .MP4 / .LRF files
      drone_2/
      ...
    flight_logs/
      drone_1/   ← DJIFlightRecord_*.txt  (and optional *.keychain.json sidecars)
      drone_2/
      ...
  mission_2/
    ...

Configure

Open config.yaml and fill in your dataset identity, species, location, and platform fields. Every option is documented inline.

dataset:
  name: my-dataset-name
  institution: "My Institution"
taxon:
  scientificName: "Equus quagga"
location:
  country: Kenya
  locality: "Ol Pejeta Conservancy"
platform:
  aircraftModel: "DJI Mini 4 Pro"

Run

export DJI_API_KEY="your_key_here"   # or skip if keychains already cached
python run_pipeline.py               # processes all missions

Selective options:

python run_pipeline.py --missions mission_1          # single mission
python run_pipeline.py --skip-flight-logs            # reuse existing raw.csv
python run_pipeline.py --skip-assemble               # skip dataset copy step
python run_pipeline.py --config path/to/config.yaml  # custom config

Outputs

output/
  mission_1/
    occurrences/           ← per-frame SRT telemetry CSVs (one per clip)
    sync/                  ← cross-drone synchronised frame table
    flight_logs/raw.csv    ← decoded DJI binary logs
    occurrences_enriched/  ← per-frame CSVs + flight-log columns joined
    events/                ← Darwin Core video_events.csv + session_events.csv
dataset_output/            ← assembled HuggingFace-ready directory

---

Glossary

• AGL: Above Ground Level — altitude measured from terrain surface
• ASL: Above Sea Level — absolute altitude
• Darwin Core: Biodiversity data standard maintained by TDWG
• FAIR²: FAIR principles extended for AI-ready and drone-specific datasets
• SRT: SubRip subtitle format; used by DJI for embedding per-frame telemetry sidecar files
• sync_utc_ms: Unix epoch milliseconds in UTC — universal synchronisation key across all drones and data streams
• TDWG: Biodiversity Information Standards (Taxonomic Databases Working Group)
• UAV: Unmanned Aerial Vehicle (drone)
• WGS84: World Geodetic System 1984 — standard GPS coordinate reference system
• WKT: Well-Known Text format for geographic geometries

Dataset Card Authors

Edouard Rolland

Dataset Card Contact

For questions about this dataset:

• Primary Contact: Edouard Rolland
• GitHub: @edouardrolland
• Issues: GitHub repository issues
• HuggingFace: https://huggingface.co/datasets/edouard-rolland/multi-perspective-dataset-plain-zebras

---

Version History:

• v1.0.0 (2026-03-23): Initial release

---

This dataset card follows the FAIR² Drone Data Standard and is modelled on the KABR Behavior Telemetry dataset card.