Envisaging a global infrastructure to exploit the potential of digitised collections

• Autor(a) principal: Groom, Quentin
• Data de Publicação: 2023
• Outros Autores: Dillen, Mathias, Addink, Wouter, Ariño, Arturo H., Bölling, Christian, Bonnet, Pierre, Cecchi, Lorenzo, Ellwood, Elizabeth R., Figueira, Rui, Gagnier, Pierre-Yves, Grace, Olwen, Güntsch, Anton, Hardy, Helen, Huybrechts, Pieter, Hyam, Roger, Joly, Alexis, Kommineni, Vamsi Krishna, Larridon, Isabel, Livermore, Laurence, Lopes, Ricardo Jorge, Meeus, Sofie, Miller, Jeremy, Milleville, Kenzo, Panda, Renato, Pignal, Marc, Poelen, Jorrit, Ristevski, Blagoj, Robertson, Tim, Rufino, Ana C., Santos, Joaquim, Schermer, Maarten, Scott, Ben, Seltmann, Katja, Teixeira, Heliana, Trekels, Maarten, Gaikwad, Jitendra
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.26/48471
• topic: machine learning; functional traits; species identification; biodiversity, specimens; computer vision

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.