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<p>Accurate wildlife re-identification is critical for a wide range of ecological studies, including density estimation via capture–recapture, demographic analyses, and behavioral research. We pr esent GIRAFFE (Generalized Image-based Re-identification using AI for Fauna Feature Extraction), a system for automated re-identification of giraffes with extensibility to other species. Our approach uses local feature matching to identify known individuals and partition unknown individuals for label annotation at scale. Further, we develop a user interface that enables both technical and non-technical users to curate large datasets and analyze repeat survey data. In contrast with existing methods that require manual labeling to facilitate individual re-identification, GIRAFFE automates key steps in the re-identification pipeline, reducing manual effort while maintaining accuracy and interpretability. Validated on real-world giraffe datasets, the system achieves over 0.9 accuracy across nine standard metrics, with some metrics achieving near perfect score. It runs 120 times faster than baseline methods and delivers a 132-fold improvement in cost-effectiveness. This supports endangered species tracking, improves analysis of population dynamics and movement patterns, and, ultimately, allows for the implementation of data-driven conservation strategies.</p>

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An accurate, efficient, and accessible AI-powered solution for wildlife re-identification in conservation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

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  • Shahrzad Gholami,
  • Derek E. Lee,
  • Caleb Robinson,
  • Monica L. Bond,
  • Rahul Dodhia,
  • Juan M. Lavista Ferres

摘要

Accurate wildlife re-identification is critical for a wide range of ecological studies, including density estimation via capture–recapture, demographic analyses, and behavioral research. We pr esent GIRAFFE (Generalized Image-based Re-identification using AI for Fauna Feature Extraction), a system for automated re-identification of giraffes with extensibility to other species. Our approach uses local feature matching to identify known individuals and partition unknown individuals for label annotation at scale. Further, we develop a user interface that enables both technical and non-technical users to curate large datasets and analyze repeat survey data. In contrast with existing methods that require manual labeling to facilitate individual re-identification, GIRAFFE automates key steps in the re-identification pipeline, reducing manual effort while maintaining accuracy and interpretability. Validated on real-world giraffe datasets, the system achieves over 0.9 accuracy across nine standard metrics, with some metrics achieving near perfect score. It runs 120 times faster than baseline methods and delivers a 132-fold improvement in cost-effectiveness. This supports endangered species tracking, improves analysis of population dynamics and movement patterns, and, ultimately, allows for the implementation of data-driven conservation strategies.