  This paper introduces the first public large-scale, long-span dataset with
sea turtle photographs captured in the wild --
\href{https://www.kaggle.com/datasets/wildlifedatasets/seaturtleid2022}{SeaTurtleID2022}.
The dataset contains 8729 photographs of 438 unique individuals collected
within 13 years, making it the longest-spanned dataset for animal
re-identification. All photographs include various annotations, e.g., identity,
encounter timestamp, and body parts segmentation masks. Instead of standard
"random" splits, the dataset allows for two realistic and ecologically
motivated splits: (i) a \textit{time-aware closed-set} with training,
validation, and test data from different days/years, and (ii) a
\textit{time-aware open-set} with new unknown individuals in test and
validation sets. We show that time-aware splits are essential for benchmarking
re-identification methods, as random splits lead to performance overestimation.
Furthermore, a baseline instance segmentation and re-identification performance
over various body parts is provided. Finally, an end-to-end system for sea
turtle re-identification is proposed and evaluated. The proposed system based
on Hybrid Task Cascade for head instance segmentation and ArcFace-trained
feature-extractor achieved an accuracy of 86.8\%.
