BIIGLE - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN

• Published in: OCEANS 2009-EUROPE
• Main Authors: Ontrup, Jörg, Ehnert, Nils, Bergmann, Melanie, Nattkemper, Tim Wilhelm
• Format: Conference Object
• Language: English
• Published: 2009
• Subjects: semantic data bases; Science 2.0; marine biology; internet; Biigle; information system; HAUSGARTEN; WEB 2.0; web; taxonomic labeling; Arctic
• Online Access: https://pub.uni-bielefeld.de/record/2018422

Ontrup J, Ehnert N, Bergmann M, Nattkemper TW. BIIGLE - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN. In: OCEANS 2009 - Europe . Bremen; 2009. Deep-sea research relies strongly on the use of high resolution cameras which generate large quantities of footage. The material can currently, however, often not be used to its full potential as the analysis is time- and labour-intensive and requires the input of many different taxonomic experts. Here, we present a system which enables the collaboration of experts from various places and the application of machine-vision tools on footage from the Arctic deep-sea observatory, HAUSGARTEN. Biigle (Bielefeld Image Graphical Labeller and Explorer) is a Web 2.0 based platform containing easily uploaded images that can be accessed by collaborating scientists. Since Biigle is realised as a rich internet application, there is no need for the local installation of complex software packages. The scientists can use a standard web browser to access the image database and immediately explore or label images. They have instant access to the data submitted by other scientists and are directly involved in the emerging community. Biigle also offers an application interface for machine-vision components aiming at the automated analysis of seafloor images. As a first module, a laser point detection allows for an automated calibration of the area covered by the camera which is vital to derive faunal density estimates. The laser points were detected in all but eight of 1883 images tested in total. The combination of human expert labels and machine-vision results can be exported into spreadsheets offering a well-established standard for further data analyses. Biigle can be accessed at http://www.biigle.de with the username and the password ” for testing purposes.