In
Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), pp. 1-11Related links
Annotation
31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019) (Brussels, Belgium, November 6-8, 2019)
Publication type
Article in monograph or in proceedings

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Editor(s)
Beuls, K.
Bogaerts, B.
Bontempi, G.
Geurts, P.
Harley, N.
Lebichot, B.
Lenaerts, T.
Louppe, G.
Eecke, P. van
Organization
SW OZ BSI OLO
SW OZ DCC AI
Languages used
English (eng)
Book title
Beuls, K.; Bogaerts, B.; Bontempi, G. (ed.), Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019)
Page start
p. 1
Page end
p. 11
Subject
Cognitive artificial intelligence; Learning and PlasticityAbstract
Previous research has shown the benefits of group equivariant convolutions for image recognition tasks. With this work we apply group equivariance to the segmentation of photovoltaic (PV) panel installations in aerial photography to determine whether the benefits translate to aerial photography segmentation. We create a custom annotation of PV panel installations in two Dutch cities using open access aerial photography. We show that group equivariant versions of traditional and residual convolutional neural networks indeed perform at least as well as the traditional versions and provide better generalization.
This item appears in the following Collection(s)
- Academic publications [203856]
- Faculty of Social Sciences [27309]
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