Abstract
We introduce a semiautomated machine learning method that employs high‐resolution imagery for the species‐level classification of Antarctic pack‐ice seals. By incorporating the spatial distribution of hauled‐out seals on ice into our analytical framework, we significantly enhance the accuracy of species identification. Employing a Random Forest model, we achieved 97.4% accuracy for crabeater seals and 98.0% for Weddell seals. To further refine our classification, we included three linearity measures: mean distance to a group's regression line, straightness index, and sinuosity index. Additional variables, such as the number of neighboring seals within a 250 m radius and distance of individual seals to the sea ice edge, also contributed to improved accuracy. Our study marks a significant advancement in the development of a cost‐effective, unified Antarctic seal monitoring system, enhancing our understanding of seal spatial behavior and enabling more effective population tracking amid environmental changes.