论文标题
在道路网络上针对GNNS编码的视觉功能
Visual Feature Encoding for GNNs on Road Networks
论文作者
论文摘要
在这项工作中，我们提出了一种新颖的方法，可以通过在道路网络数据上使用应用程序来学习将视觉特征编码到图形神经网络中的编码。我们提出了一个结合了最先进的视觉主链网络与图形神经网络的体系结构。更具体地说，我们通过使用各种Resnet Architectures编码卫星图像来在开放式街道地图网络上执行道路类型分类任务。我们的体系结构进一步实现了微调和转移学习方法，可以通过在NWPU-Resisc45图像分类数据集上进行预处理，用于遥感，并将其与纯粹的Imagenet-Pretrate-Pretration Resnet模型作为视觉特征编码器进行比较。结果不仅表明，视觉功能编码器优于低级视觉特征，而且还表明，将视觉功能编码器的微调编码器（例如NWPU-Resisc45）进行微调可以进一步提高GNN在机器学习任务上的性能，例如道路类型分类。
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.
