Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation


Autoria(s): Henry, Lea-Anne; Mukherjee, Sankha S.; Robertson, Neil M.; De Clippele, Laurence; Roberts, J. Murray
Data(s)

12/09/2016

Resumo

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Formato

application/pdf

Identificador

http://pure.qub.ac.uk/portal/en/publications/deep-corals-deep-learning-moving-the-deep-net-towards-realtime-image-annotation(1512b8d3-d842-4155-9011-7c8dd2520b06).html

http://pure.qub.ac.uk/ws/files/92112999/deep_corals_deep_learning.pdf

Idioma(s)

eng

Direitos

info:eu-repo/semantics/restrictedAccess

Fonte

Henry , L-A , Mukherjee , S S , Robertson , N M , De Clippele , L & Roberts , J M 2016 , ' Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation ' 6th International Symposium on Deep-Sea Corals , Boston , United States , 11/09/2016 - 16/09/2016 , .

Tipo

conferenceObject