Applying computer vision to natural history collections for ecological, taxonomic, and conservation research

最近，由NHM等机构率先开展的自然历史收藏（NHCs）的大规模数字化工作，使全球的科学家能够访问不计其数的标本和相关的元数据（例如收集日期和地点）进行研究。利用这些数字化的收藏品，NHM及其同事正在开发新的计算机视觉技术，从生命之树和各种栖息地的NHCs中自动提取生物和历史信息。然而，至关重要的是，这些庞大的数据集现在必须专注于释放它们回答生态、分类学和保护问题的潜力。本研究将探索从NHCs中提取的大量计算机视觉数据，并将其应用于以下问题：计算机视觉能否用于填补分类空白并促进新物种的描述？从计算机视觉中提取的形态特征（如体型、形状、颜色）能否用于检查生物对气候变化的反应（如温度-尺寸反应）？计算机视觉能否作为一种手段，对从广泛的栖息地（例如森林到深海）实地采集的无脊椎动物进行快速生物多样性评估？这位博士将是第一个将计算机视觉方法应用于分类学、生态学和保护问题的人。

Recent mass digitization efforts of Natural History Collections (NHCs), pioneered by institutions like the NHM, have given scientists across the globe access to untold numbers of specimens and associated meta-data (e.g. date and location of collection) for research. Using these digitized collections, the NHM and colleagues are developing new computer vision techniques to automatically extract biological and historical information from NHCs across the tree of life and from a wide array of habitats. Crucially however, these massive datasets must now be focused towards unlocking their potential for answering ecological, taxonomic, and conservation questions. This studentship will explore the vast amount of computer vision data extracted from NHCs and apply them to the following questions: Can computer vision be used to fill taxonomic gaps and facilitate new species descriptions?Can the morphological features (e.g. body size, shape, colour) extracted from computer vision be used to examine biotic response to climate change (e.g. temperature-size responses)?Can computer vision be used as a means for rapid biodiversity assessments of field collected invertebrates from a wide range of habitats (e.g. forests to the deep sea)? This PhD will be among the first to apply computer vision methods to taxonomic, ecological, and conservation questions.