DOI: 10.5281/zenodo.14921494
title: ZakynthosTurtles Dataset

description: Description:







The ZakynthosTurtles Dataset has been designed to support the development of numerical methods for the recognition and re-identification of individual sea turtles based on their unique scale patterns. Each turtle’s distinct scale arrangement provides a natural identifier, allowing researchers to track and study individuals over time. This dataset contains photographs captured in the wild, emphasizing the importance of non-intrusive wildlife monitoring techniques. The dataset includes 40 individual loggerhead sea turtles, with each turtle represented by four high-resolution photographs—both left and right profiles taken in two different years. This structure offers unique opportunities for investigating the stability and similarity of scale patterns over time and between opposite profiles.

 







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Dataset Structure:



Species: Loggerhead sea turtles (Caretta caretta)

Total Turtles: 40 individuals

Photos per Turtle: 4 (Left and right profile taken in two different years)

Total Photos: 160 high-quality images taken underwater


This structured dataset enables scientists to study important questions in computer vision and machine learning, such as cross-year recognition and left-right profile matching, offering insights into how re-identification algorithms handle natural variations in appearances over time.

Associated Datasets:

The ZakynthosTurtles dataset is part of a larger family of sea turtle datasets, each contributing unique insights and use cases. These include:



AmvrakikosTurtles: A public dataset comprising 50 loggerhead sea turtles, captured from a boat during sea surveys. This dataset primarily focuses on surface-level photography.

ReunionTurtles: This dataset features 50 green and 34 hawksbill sea turtles, photographed underwater, providing additional variety in species and underwater imaging conditions.


Together, these datasets provide a comprehensive foundation for designing and testing machine learning models for sea turtle identification and re-identification.

Related Resources:



wildlife-datasets: A Python library summarizing various public datasets for wildlife re-identification, including additional turtle datasets.

wildlife-tools: A Python library offering tools for training and testing re-identification models using wildlife data.

Research Paper 1: This paper covers our work with another turtle dataset, featuring 438 individual sea turtles, further expanding the scope of research.

Research Paper 2: This paper details the development and application of the Python packages mentioned above, aimed at enhancing re-identification workflows.


Conclusion:

The ZakynthosTurtles dataset provides a valuable resource for advancing research in wildlife re-identification, particularly for sea turtles. Its structured format and multi-year imagery make it an excellent tool for researchers and developers working in the fields of machine learning, computer vision, and wildlife monitoring. By contributing to the refinement of individual recognition algorithms, this dataset plays a key role in non-invasive wildlife management and conservation efforts, particularly for endangered species like the loggerhead sea turtles.

This dataset is sourced from Kaggle.