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2021
Journal article
Evidence-based guidelines for developing automated conservation assessment methods [PREPRINT].
Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species have significant gaps in coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to fill these gaps, leveraging improvements in computing and digitally-available information. Choices made...Walker, Barnaby E. ; Leão, Tarciso C.C. ; Bachman, Steven P. ; Lucas, Eve ; Nic Lughadha, Eimear
Biodiversity conservation, Automation, Machine learning, IUCN Red List, and Conservation assessments
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2022
Journal article
Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.
The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these...Walker, Barnaby E. ; Tucker, Allan ; Nicolson, Nicky
Machine learning, Natural history collections, Digitized herbarium specimens, Deep learning, and Computer vision
