Datasets:

imageomics
/

GZCD

license: cc-by-4.0
language:
  - en
pretty_name: GZCD (Grevy's Zebra Census Dataset)
task_categories:
  - object-detection
  - zero-shot-image-classification
  - image-classification
tags:
  - biology
  - ecology
  - computer-vision
  - animal-re-identification
  - wildlife-conservation
  - imageomics
  - yolo
  - bioclip
  - miew-id
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - images/*.jpg
      - initial_metadata.json
      - final_metadata.json
description: >-
  A comprehensive ecological computer vision dataset designed for wildlife
  detection, species classification, and individual animal re-identification.
  Features bounding boxes, identifiability scores, and individual re-ID clusters
  processed through the 9-stage Great Grevy's Rally (GGR) pipeline.

Dataset Card for GZCD (Grevy's Zebra Census Dataset)

The GZCD (Grevy's Zebra Census Dataset) is a comprehensive ecological computer vision dataset designed for wildlife detection, species classification, and individual animal re-identification (Re-ID). Captured primarily in the field, this dataset emphasizes endangered equid and ungulate populations, specifically Grevy's zebras, Plains zebras, and Reticulated giraffes.

GZCD consists of field photographs captured in Meru County, Kenya, taken over four days by 13 photographers during 2016 and 2018 iterations of the Great Grevy's Rally (GGR). The data collection was carried out by citizen scientists and conservationists across a 25,000 square kilometer range. It was curated to help train and test new computer vision algorithms, most notably building upon the pivotal animal re-identification work by Jason Parham.

Dataset Details

Dataset Description

Curated by: Imageomics Institute
Repository: Imageomics/GZCD
Pipeline Code: Imageomics/GGR_pipeline

This dataset provides raw ecological image data alongside the carefully processed outputs of the GGR Pipeline. It serves as an ideal resource for researchers bridging Computer Vision (CV) and ecology, providing manual ground-truth and annotations for bounding boxes, viewpoints, census annotation scores, and individual animal Re-ID clusters obtained after applying the semi-automated GGR pipeline.

Dataset Structure

The dataset contains the raw images and two metadata files representing the data before and after processing through the GGR pipeline.

text
/dataset/
    images/
        <uuid 1>.jpg
        <uuid 2>.jpg
        ...
    initial_metadata.json
    final_metadata.json

Data Instances

images/: The raw field images capturing various wildlife species.
initial_metadata.json: Represents the raw state of the dataset prior to processing, containing EXIF data, camera body details, flash card source data, GPS coordinates, and initial image dimensions.
final_metadata.json: The fully annotated dataset resulting from the 9-stage CV pipeline, containing localized bounding boxes, species, viewpoint and census annotation classifications, and individual IDs.

Data Fields

`initial_metadata.json`

Contains metadata of the image files before running the pipeline:

gid: Integer global ID.
uuid: Unique string identifier for the image.
uri / uri_original / original_name / original_path: File paths and names pointing to the images/ directory.
ext: File extension (e.g., .jpg).
width / height: Image dimensions in pixels.
time_posix: Epoch timestamp of image capture.
gps_lat / gps_lon: Geographic coordinates of the image capture. Note: Missing, masked, or unknown GPS coordinates are explicitly encoded as -1.0.
orientation: Camera orientation integer.
location_code: Dataset specific location code (e.g., "GZCD").
camera_make / camera_model: Hardware metadata extracted from image EXIF data.
card_id: Identifier string tracking the specific physical storage card used during field data ingestion.

`final_metadata.json`

Contains cleaned image metadata and a rich annotations array generated by the GGR pipeline:

Image Data: uuid, image_path, datetime, latitude, longitude (encoded as -1.0 if unknown), width, height, camera_make, camera_model, card_id.
Annotations:
- image_uuid: Links the annotation to the parent image.
- bbox: Absolute bounding box [x, y, width, height] of the detected animal.
- region_bbox: A localized bounding box of census annotation region.
- category_id: Integer corresponding to the species category list.
- viewpoint: String denoting viewpoint orientation (e.g., right, left).
- CA_score: Float representing the Identifiable Annotation (IA) confidence score.
- annotations_census: Boolean indicating if the annotation is valid for population counting.
- annot_uuid: Unique identifier for the specific detection.
- manual: Boolean indicating if the annotation underwent manual verification.
- individual_id: UUID grouping annotations belonging to the exact same individual animal across different images for Re-ID.

Dataset Creation

Data Collection and Processing

The images were collected in Meru County, Kenya during the 2016 and 2018 census rallies. This dataset is the direct output of the GGR Pipeline, a 9-stage workflow designed for robust ecological tracking:

Import: Parses raw field data, ingests card/camera metadata, and generates standard metadata descriptions, mapping absolute paths to the images.
Detection: Utilizes YOLO to localize animals in the images, creating initial bounding boxes (bbox).
Species Classification: Processes detections through BioCLIP to classify the species (e.g., Grevy's zebra vs. Plains zebra).
Viewpoint Classification: Determines the orientation of the detected animal (up, front, back, left, right).
Census Annotation (CA) Classification: Evaluates photographic quality and pose. Assigns a CA_score and a boolean flag indicating if the animal's features (e.g., stripes) are clear enough for individual identification.
CA Filtering: Discards annotations below the identifiability threshold decided empirically and simplifies viewpoints strictly to left or right flanks for Re-ID purposes.
MiewID: Generates deep metric embeddings for all filtered high-quality annotations leveraging the MiewID feature extraction model.
Local Clusters and Alternatives (LCA) Algorithm: Clusters the generated embeddings based on feature similarity, grouping annotations of the same individual animal together by resolving inconsistencies.

Considerations for Using the Data

Missing Location Data: When GPS coordinates could not be resolved from field equipment or manual logs, fields for latitude and longitude (gps_lat/gps_lon) default to -1.0. Algorithms utilizing geometric filtering should catch these sentinel values.
Viewpoint Filtering: Because Re-ID of Grevy's Zebra relies on right-flank stripe patterns, the pipeline aggressively selects right viewpoints whose embeddings are extracted and ID assigned. Other viewpoints are excluded from ID assignment.
Geographic Focus: The spatial data anchors tightly to the Meru County ecosystem in Kenya. Models trained heavily on this dataset may experience domain shift when deployed in different biomes or under disparate lighting conditions.
Class Imbalance: Due to the targeted focus of the Great Grevy's Rally on Grevy's Zebras, the final JSON contains individual IDs for Grevy's Zebra only.

Citations & References

If you use this dataset, please cite both the dataset release and the foundational computer vision framework developed for scaling up citizen science wildlife censuses:

@misc{gzcd2026,
  author = {Ankit K. Upadhyay and Ekaterina Nepovinnykh and S M Rayeed and Aidan Westphal and Lawrence Miao and Julian Bain and Jaeseok Kang and Tuomas Eerola and Heikki Kälviäinen and Charles V. Stewart},
  title = {GZCD: Grevy's Zebra Census Dataset},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/datasets/imageomics/GZCD](https://huggingface.co/datasets/imageomics/GZCD)}}
}

@inproceedings{parham2017animal,
  author    = {Jason Parham and Jonathan Crall and Charles Stewart and Tanya Berger-Wolf and Daniel I. Rubenstein},
  title     = {Animal Population Censusing at Scale with Citizen Science and Photographic Identification},
  booktitle = {Proceedings of the AAAI 2017 Spring Symposium on AI for Social Good},
  address   = {Stanford, CA},
  year      = {2017}
}

Dataset Card Authors

S M Rayeed
Elizabeth G. Campolongo