Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs

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Title:	Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs
Author(s):	Bokkers, L.A.R. ; Ambrogioni, L. ; Güçlü, U.
Publication year:	2019
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. 1-11
Publisher:	[S.l. : s.n.]
Related links:	http://ceur%2Dws.org/Vol%2D2491/paper105.pdf
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
Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/215842
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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
Subject:	Cognitive artificial intelligence Learning and Plasticity
Organization:	SW OZ BSI OLO SW OZ DCC AI
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
Abstract:	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.
Languages used:	English (eng)