GNN

We propose a method that combines a Fully Convolutional Network and a Graph Neural Network to efficiently extract road networks from aerial images in a single pass.

We present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy.