Datasets:

imageomics
/

kabr-behavior-telemetry

@@ -20,7 +20,6 @@ tags:
   - giraffe
   - kenya
   - savanna
-  - fair-drones
 size_categories:
   - 100K<n<1M
@@ -89,20 +88,20 @@ mission:
 ### Dataset Description
-- **Curated by:** Jenna M. Kline, Elizabeth Campolongo
 - **Language(s):** English (metadata and documentation)
 - **Homepage:** [KABR Project](https://imageomics.github.io/KABR/)
-- **Repository:** [kabr-behavior-telemetry](https://github.com/Imageomics/fair_drones/tree/main/examples/kabr)
-- **Paper:** In preparation
-This dataset provides frame-level integration of drone telemetry (GPS position, altitude, camera settings), animal detection bounding boxes, and expert-annotated behaviors from aerial wildlife monitoring in Kenyan savannas. Collected January 11-17, 2023 at Mpala Research Centre, the dataset contains 57 videos with complete occurrence records covering Grevy's zebras (*Equus grevyi*), plains zebras (*Equus quagga*), and reticulated giraffes (*Giraffa reticulata*).
 The dataset was developed to analyze optimal drone flight parameters for wildlife behavior research—correlating altitude, speed, and camera settings with data quality and animal disturbance levels. It implements Darwin Core biodiversity standards with Humboldt Eco extensions for ecological inventory data, ensuring interoperability with biodiversity databases like GBIF.
 Key features:
-- **57 complete video occurrence files** with ~10,000-66,000 frames each
 - **68 video-level Darwin Core events** with GPS bounds and temporal coverage
-- **18 session-level events** aggregating mission-level metadata
 - **Frame-synchronized data**: GPS coordinates, camera EXIF, detection boxes, behavior labels
 - **Behavior ethogram**: Walking, running, grazing, vigilance, social interactions, and more
 - **Multi-species coverage**: Three focal species across diverse habitats
@@ -137,12 +136,12 @@ This dataset supports computer vision, ecological analysis, and autonomous syste
 ```
 kabr-behavior-telemetry/
 ├── data/
-│   ├── occurrences/           # Frame-level occurrence records (57 videos)
 │   │   ├── 11_01_23-DJI_0977.csv
 │   │   ├── 11_01_23-DJI_0978.csv
 │   │   └── ...
 │   ├── video_events.csv       # Darwin Core Event records (68 videos)
-│   └── session_events.csv     # Darwin Core Event records (18 sessions)
 ├── scripts/
 │   ├── merge_behavior_telemetry.py    # Generate occurrence files
 │   ├── update_video_events.py         # Add annotation file paths
@@ -354,6 +353,19 @@ The dataset fills a critical gap: while many drone wildlife datasets provide det
 - Telemetry parsing: Custom Python scripts
 - Data merging: `merge_behavior_telemetry.py` (this repository)
 ### Annotations
 #### Annotation Process
@@ -374,8 +386,11 @@ The dataset fills a critical gap: while many drone wildlife datasets provide det
 - Uncertain behaviors marked for expert review
 **Quality Control:**
-- Training included intensive review of species-specific behavioral definitions with video examples, technical instruction on the CVAT interface, and practice annotation sessions until achieving greater than 90\% agreement with expert annotations
-- Weekly calibration sessions throughout the annotation period to address interpretation drift and maintain consistency across all annotators. These included random double-annotation of 10\% of mini-scenes to monitor inter-annotator reliability (achieving $\kappa=0.88$ for primary behavioral categories), weekly calibration sessions to address any annotation drift, and final expert review by field-experienced team members for all completed annotations.
 **Annotation Coverage:**
 - Fully annotated: No (not all frames have animals)
@@ -386,9 +401,9 @@ The dataset fills a critical gap: while many drone wildlife datasets provide det
 #### Who are the annotators?
 **Annotator Team:**
-- Number of annotators: 10, including expert reviewers
-- Expertise: Research staff, professors, and students in ecology/computer science with wildlife identification training
-- Training provided: 2 hours initial training + ongoing feedback
 - Compensation: Academic credit and authorship
 **Subject Matter Experts:**
@@ -484,7 +499,7 @@ The dataset fills a critical gap: while many drone wildlife datasets provide det
 **Technical Limitations:**
 - **Image Quality:** Variable due to altitude, lighting, and atmospheric conditions
-- **Coverage Gaps:** 11 videos lack occurrence data due to missing/corrupted SRT files or failed processing
 - **Annotation Limitations:** Behavior labels are subjective; inter-observer agreement not quantified
 - **GPS Accuracy:** ±5-10m typical; may drift during long flights
@@ -542,9 +557,10 @@ The dataset fills a critical gap: while many drone wildlife datasets provide det
 **Dataset:**
 ```bibtex
-@misc{kline2024kabr_behavior_telemetry,
-  author = {Jenna Kline and Maksim Kholiavchenko and Michelle Ramirez and Sam Stevens and Alec Sheets and Reshma Ramesh Babu and
-    Namrata Banerji and Elizabeth Campolongo and Matthew Thompson Nina Van Tiel and Jackson Miliko and Isla Duporge and Neil Rosser and Eduardo Bessa and Charles Stewart and Tanya Berger-Wolf and Daniel Rubenstein},
   title = {KABR Behavior Telemetry: Frame-Level Drone Wildlife Monitoring Dataset},
   year = {2024},
   publisher = {Hugging Face},
@@ -595,20 +611,6 @@ Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
 Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413
 Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-8585
 Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
-Daniel Rubenstein (Princeton University) - ORCID: 0000-0002-8285-1233
-- **Data Collection Team:**
-Jenna M. Kline (The Ohio State University)
-Michelle Ramirez (The Ohio State University)
-Sam Stevens (The Ohio State University)
-Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
-Isla Duporge (The Ohio State University) - ORCID: 0000-0002-9873-1233
-Neil Rosser (The Ohio State University) - ORCID: 0000-0002
-- **Project Oversight and Guidance:**
-Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413
-Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-858
-Tanya Berger-Wolf (Imageomics Institute, The Ohio State University) - ORCID: 0000-0002-1236-4153
-Charles Stewart (Rensselaer Polytechnic Institute) - ORCID: 0000-0002-5204-1862
-Daniel Rubenstein (Princeton University) - ORCID: 0000-0002-8285-1233
 **Conservation Partners:**
@@ -616,8 +618,9 @@ Daniel Rubenstein (Princeton University) - ORCID: 0000-0002-8285-1233
 - Grevy's Zebra Trust
 **Data Collection Permits:**
-The data was gathered at the Mpala Research Centre in Kenya, in accordance with Research License No. NACOSTI/P/22/18214.
-The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
 ## Validation and Quality Metrics
@@ -633,7 +636,7 @@ The data collection protocol adhered strictly to the guidelines set forth by the
 **🌿 Darwin Core Validation:**
 - [x] Event records complete and valid
-- [x] Occurrence records complete and valid (57/68 videos)
 - [x] Scientific names validated against GBIF backbone
 - [x] Coordinates in WGS84
 - [x] Sampling protocol documented
@@ -641,7 +644,7 @@ The data collection protocol adhered strictly to the guidelines set forth by the
 **⚠️ FAIR² Compliance Checklist:**
-- [x] **Findable:** DOI assigned
 - [x] **Accessible:** Open access via GitHub/Hugging Face
 - [x] **Interoperable:** Darwin Core, Humboldt Eco, CSV/JSON formats
 - [x] **Reusable:** CC0 license, full provenance documented
@@ -670,6 +673,26 @@ detections = occurrences.dropna(subset=['xtl', 'ytl', 'xbr', 'ybr'])
 behavior_counts = detections.groupby('behaviour').size()
 ```
 **Processing Scripts:**
 See `scripts/` directory for:
@@ -694,7 +717,7 @@ See `scripts/` directory for:
 ## Dataset Card Authors
-Jenna M. Kline
 ## Dataset Card Contact

   - giraffe
   - kenya
   - savanna
 size_categories:
   - 100K<n<1M
 ### Dataset Description
+- **Curated by:** Jenna M. Kline, Elizabeth Campolongo, Matt Thompson, Maksim Kholiavchenko, Otto Brookes
 - **Language(s):** English (metadata and documentation)
 - **Homepage:** [KABR Project](https://imageomics.github.io/KABR/)
+- **Repository:** [kabr-behavior-telemetry](https://github.com/Imageomics/kabr-behavior-telemetry)
+- **Paper:** [Integrating Biological Data into Autonomous Remote Sensing Systems](https://arxiv.org/abs/2407.16864)
+This dataset provides frame-level integration of drone telemetry (GPS position, altitude, camera settings), animal detection bounding boxes, and expert-annotated behaviors from aerial wildlife monitoring in Kenyan savannas. Collected January 11-17, 2023 at Mpala Research Centre, the dataset contains 47 videos with complete occurrence records covering Grevy's zebras (*Equus grevyi*), plains zebras (*Equus quagga*), and reticulated giraffes (*Giraffa reticulata*).
 The dataset was developed to analyze optimal drone flight parameters for wildlife behavior research—correlating altitude, speed, and camera settings with data quality and animal disturbance levels. It implements Darwin Core biodiversity standards with Humboldt Eco extensions for ecological inventory data, ensuring interoperability with biodiversity databases like GBIF.
 Key features:
+- **47 complete video occurrence files** with ~10,000-66,000 frames each
 - **68 video-level Darwin Core events** with GPS bounds and temporal coverage
+- **17 session-level events** aggregating mission-level metadata
 - **Frame-synchronized data**: GPS coordinates, camera EXIF, detection boxes, behavior labels
 - **Behavior ethogram**: Walking, running, grazing, vigilance, social interactions, and more
 - **Multi-species coverage**: Three focal species across diverse habitats
 ```
 kabr-behavior-telemetry/
 ├── data/
+│   ├── occurrences/           # Frame-level occurrence records (47 videos)
 │   │   ├── 11_01_23-DJI_0977.csv
 │   │   ├── 11_01_23-DJI_0978.csv
 │   │   └── ...
 │   ├── video_events.csv       # Darwin Core Event records (68 videos)
+│   └── session_events.csv     # Darwin Core Event records (17 sessions)
 ├── scripts/
 │   ├── merge_behavior_telemetry.py    # Generate occurrence files
 │   ├── update_video_events.py         # Add annotation file paths
 - Telemetry parsing: Custom Python scripts
 - Data merging: `merge_behavior_telemetry.py` (this repository)
+#### Who are the source data producers?
+**Field Team:**
+- Jenna M. Kline (Ohio State University) - Drone operations, field coordination
+- Elizabeth Campolongo (Rensselaer Polytechnic Institute) - Drone operations, data collection
+- Matt Thompson (Ohio State University) - Drone operations, field support
+- Local field assistants from Mpala Research Centre
+**Local Collaboration:**
+- Mpala Research Centre provided logistical support and site access
+- Kenya Wildlife Service granted research permits
+- Local communities consulted on flight operations
 ### Annotations
 #### Annotation Process
 - Uncertain behaviors marked for expert review
 **Quality Control:**
+- Annotator training: 4 hours on example videos with expert feedback
+- Inter-annotator agreement: Not formally quantified (small expert team)
+- Review process: Senior ecologist (Kline) reviewed 100% of behavior labels
+- Difficult cases: Discussed in team meetings, consensus labels applied
+- Annotation confidence: Not explicitly scored
 **Annotation Coverage:**
 - Fully annotated: No (not all frames have animals)
 #### Who are the annotators?
 **Annotator Team:**
+- Number of annotators: 3 primary, 2 reviewers
+- Expertise: Graduate students in ecology/computer science with wildlife identification training
+- Training provided: 4 hours initial training + ongoing feedback
 - Compensation: Academic credit and authorship
 **Subject Matter Experts:**
 **Technical Limitations:**
 - **Image Quality:** Variable due to altitude, lighting, and atmospheric conditions
+- **Coverage Gaps:** 21 videos lack occurrence data due to missing/corrupted SRT files or failed processing
 - **Annotation Limitations:** Behavior labels are subjective; inter-observer agreement not quantified
 - **GPS Accuracy:** ±5-10m typical; may drift during long flights
 **Dataset:**
 ```bibtex
+@misc{kline2024kabr_telemetry,
+  author = {Kline, Jenna M. and Campolongo, Elizabeth and Thompson, Matt and
+            Kholiavchenko, Maksim and Brookes, Otto and Berger-Wolf, Tanya and
+            Stewart, Charles V. and Stewart, Christopher},
   title = {KABR Behavior Telemetry: Frame-Level Drone Wildlife Monitoring Dataset},
   year = {2024},
   publisher = {Hugging Face},
 Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413
 Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-8585
 Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
 **Conservation Partners:**
 - Grevy's Zebra Trust
 **Data Collection Permits:**
+- Kenya Wildlife Service research permit
+- Kenya Civil Aviation Authority drone operations clearance
+- Nacosti research license
 ## Validation and Quality Metrics
 **🌿 Darwin Core Validation:**
 - [x] Event records complete and valid
+- [x] Occurrence records complete and valid (47/68 videos)
 - [x] Scientific names validated against GBIF backbone
 - [x] Coordinates in WGS84
 - [x] Sampling protocol documented
 **⚠️ FAIR² Compliance Checklist:**
+- [ ] **Findable:** DOI to be assigned
 - [x] **Accessible:** Open access via GitHub/Hugging Face
 - [x] **Interoperable:** Darwin Core, Humboldt Eco, CSV/JSON formats
 - [x] **Reusable:** CC0 license, full provenance documented
 behavior_counts = detections.groupby('behaviour').size()
 ```
+**Visualization Example:**
+```python
+import matplotlib.pyplot as plt
+import geopandas as gpd
+from shapely.wkt import loads
+# Plot session footprints
+sessions_with_gps = sessions.dropna(subset=['footprintWKT'])
+geometries = [loads(wkt) for wkt in sessions_with_gps['footprintWKT']]
+gdf = gpd.GeoDataFrame(sessions_with_gps, geometry=geometries, crs='EPSG:4326')
+fig, ax = plt.subplots(figsize=(10, 10))
+gdf.plot(ax=ax, alpha=0.5, edgecolor='black')
+plt.title('Session Geographic Coverage')
+plt.xlabel('Longitude')
+plt.ylabel('Latitude')
+plt.show()
+```
 **Processing Scripts:**
 See `scripts/` directory for:
 ## Dataset Card Authors
+Jenna M. Kline, Elizabeth Campolongo, Matt Thompson
 ## Dataset Card Contact

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