## Frontmatter | | | | --- | --- | | Authors | [[Lars Bokkers]], [[Luca Ambrogioni]], [[Umut Güçlü]] | | Date | 2019/11 | | Source | [[Benelux Conference on Machine Learning]] | | URL | https://ceur-ws.org/Vol-2491/paper105.pdf | | Citation | Bokkers, L., Ambrogioni, L., & Güçlü, U. (2019). [[Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs]]. In _Benelux Conference on Machine Learning_. [[URL](https://ceur-ws.org/Vol-2491/paper105.pdf)]. #Conference | ## 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 ## PDF ![[Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs.pdf]]