  This paper introduces the first public large-scale, long-span dataset with
sea turtle photographs captured in the wild -- SeaTurtleID2022
(https://www.kaggle.com/datasets/wildlifedatasets/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
time-aware closed-set with training, validation, and test data from different
days/years, and (ii) a 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%.
