  In this work, we present a novel approach to learning an encoding of visual
features into graph neural networks with the application on road network data.
We propose an architecture that combines state-of-the-art vision backbone
networks with graph neural networks. More specifically, we perform a road type
classification task on an Open Street Map road network through encoding of
satellite imagery using various ResNet architectures. Our architecture further
enables fine-tuning and a transfer-learning approach is evaluated by
pretraining on the NWPU-RESISC45 image classification dataset for remote
sensing and comparing them to purely ImageNet-pretrained ResNet models as
visual feature encoders. The results show not only that the visual feature
encoders are superior to low-level visual features, but also that the
fine-tuning of the visual feature encoder to a general remote sensing dataset
such as NWPU-RESISC45 can further improve the performance of a GNN on a machine
learning task like road type classification.
