Computer Vision for Natural Heritage (CVNH) Call for Papers

 The Computer Vision for Natural Heritage (CVNH) 
is an ECCV workshop
 that brings together computer vision researchers, natural heritage
 digitization experts, and domain scientists to advance methods for
 analyzing 2D, 3D, and multi-modal imaging of natural history
 collections and to identify open challenges.

* Abstract: Globally, large-scale digitization initiatives are
  generating massive image datasets from natural history
  collections. Computer vision is essential for unlocking the
  scientific value of these data, enabling automated extraction of
  specimen information and supporting research in biodiversity,
  ecology, and evolution, including studies of migration and ongoing
  mass extinction. Despite controlled imaging conditions and rich
  metadata, automated analysis remains challenging due to complex
  specimen structures, varying appearances, and handwritten
  labels. Natural history datasets span 2D images (e.g., photographs,
  multi-spectral scans), 3D volumetric data (e.g., micro-CT), and
  multi-modal inputs (e.g., image-text pairs).

* Call for Papers: The covered topics include but are not limited to:

    Digitization and mobilisation of specimen data from labels and archive cards
    2D and 3D specimen imaging

    Robotic vision for automated imaging
    Species recognition and AI-assisted species description
    Phenology estimation from collection specimens
    Benchmarks, datasets, and evaluation protocols
    Vision-based quality control and error detection
    Morphological trait extraction using CV
    Computational challenges for species recognition (CT-scans of micro-fossils and handwritten insect labels)


* Kaggle challenges: We are also launching two Kaggle Challenges. The
  authors of the top-performing submissions will be invited to
  participate in a paper.

    Foram2026 Challenge: Detection and classification of microCT 3D
    scans of Forameniferas.

    SCAT2026: Text recognition and text type identification (e.g.,
    "date", "locality") in museum label photographs.


* Speakers: Elizabeth G. Campolongo (Senior Data Scientist for the
  Imageomics Institute, The Ohio State University (United States));
  Emily Baird (Professor at the Stockholm University (Sweden)); Moritz
  Lürig (Assistant Professor at Bonn University (Germany)); Joakim
  Bruslund Haurum (Assistant Professor at the University of Southern
  Denmark (Denmark)).

* Organizers: Kim Steenstrup Pedersen (Professor, Natural History
  Museum Denmark, University of Copenhagen); Anders Bjorholm Dahl
  (Professor, Technical University of Denmark & QIM); Hans Martin Kjer
  (Associate Professor, Technical University of Denmark & QIM);
  Roberta Eleanor Hunt (Postdoctoral Researcher, University of
  Copenhagen); J. Miguel Valverde (Postdoctoral Researcher, Technical
  University of Denmark & QIM).

* Date: September 8th or 9th, 2026 (TBA)
* Location: Malmö (Sweden)
* Website: 
https://computer-vision-for-natural-heritage.github.io/