Visual Feature Encoding for GNNs on Road Networks

• URL: http://arxiv.org/abs/2203.01187v1
• Date: Wed, 2 Mar 2022 15:37:50 GMT
• Title: Visual Feature Encoding for GNNs on Road Networks
• Authors: Oliver Stromann, Alireza Razavi and Michael Felsberg
• Abstract summary: We propose an architecture that combines vision backbone networks with graph neural networks. We perform a road type classification task on an Open Street Map road network through encoding of satellite imagery. Our architecture further enables fine-tuning and a transfer-learning approach is evaluated by pretraining.
• Score: 14.274582421372308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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