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<p>We introduce <a href="https://doi.org/10.5281/zenodo.17592004">CzechLynx</a>, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (<i>Lynx lynx</i>). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.</p>

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CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Lukas Picek,
  • Jakub Straka,
  • Miroslav Jirik,
  • Elisa Belotti,
  • Martin Duľa,
  • Josefa Krausová,
  • Michal Bojda,
  • Vojtech Cermak,
  • Luděk Bufka,
  • Rostislav Dvořák,
  • Luboslav Hrdý,
  • Václav Kocourek,
  • Jiří Labuda,
  • Luděk Toman,
  • Vlado Trulík,
  • Martin Váňa,
  • Miroslav Kutal

摘要

We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.