Envisaging a global infrastructure to exploit the potential of digitised collections
2022
Groom, Quentin | Dillen, Mathias | Addink, Wouter | Ariño, Arturo | Bölling, Christian | Bonnet, Pierre | Cecchi, Lorenzo | Ellwood, Elizabeth | Figueira, Rui | Gagnier, Pierre-Yves | Grace, Olwen | Güntsch, Anton | Hardy, Helen | Huybrechts, Pieter | Hyam, Roger | Joly, Alexis | Larridon, Isabel | Kommineni, Vamsi Krishna | Livermore, Laurence | Lopes, Ricardo Jorge | Miller, Jeremy | Meeus, Sofie | Milleville, Kenzo | Pignal, Marc | Panda, Renato | Poelen, Jorrit | Ristevski, Blagoj | Robertson, Tim | Rufino, Cristina | Santos, Joaquim | Schermer, Maarten | Seltmann, Katja | Scott, Ben | Teixeira, Heliana | Trekels, Maarten | Gaikwad, Jitendra | Meise Botanic Garden [Belgium] (Plantentuin) | Naturalis Biodiversity Center [Leiden] | Museum für Naturkunde [Berlin] | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Museo di Storia Naturale di Firenze ; Università degli Studi di Firenze = University of Florence (UniFI) | Instituto Superior de Agronomia [Lisboa] (ISA) ; Universidade de Lisboa = University of Lisbon (ULISBOA) | Muséum national d'Histoire naturelle (MNHN) | Royal Botanic Garden [Edinburgh] | Scientific Data Management (LIRMM | ZENITH) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) | Royal Botanic Gardens [Kew] | Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany] | Universidade do Porto = University of Porto | Internet Technology and Data Science Lab (IDLab) ; Universiteit Antwerpen = University of Antwerpen [Antwerpen]-Universiteit Gent = Ghent University (UGENT) | Global Biodiversity Information Facility (GBIF) | Universidade de Aveiro
Tens of millions of images from biological collections have become available online in 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. Whilst image analysis has become mainstream in consumer applications, it is still only used 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 the images of collection objects could be made accessible as a single corpus. In this paper, we make the case for building infrastructure that could support image analysis of collection objects. We show that such an infrastructure is entirely feasible and well worth the investment.
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