AU3-GAN: A Method for Extracting Roads from Historical Maps Based on an Attention Generative Adversarial Network
Yao Zhao, Guangxia Wang, Jian Yang, Tingting Li, Ziwei Li
Abstract
Abstract In recent years, the integration of deep learning technology based on convolutional neural networks with historical maps has made it possible to automatically extract roads from these maps, which is highly important for studying the evolution of transportation networks. However, the similarity between roads and other features (such as contours, water systems, and administrative boundaries) poses a significant challenge to the feature extraction capabilities of convolutional neural networks (CNN). Additionally, CNN require a large quantity of labelled data for training, which can be a complex issue for historical maps. To address these limitations, we propose a method for extracting roads from historical maps based on an attention generative adversarial network. This approach leverages the unique architecture and training methodology of the generative adversarial network to augment datasets by generating data that closely resembles real samples. Meanwhile, we introduce an attention mechanism to enhance UNet3 + and achieve accurate historical map road segmentation images. We validate our method using the Third Military Mapping Survey of Austria-Hungary and compare it with a typical U-shaped network. The experimental results show that our proposed method outperforms the direct use of the U-shaped network, achieving at least an 18.26% increase in F1 and a 7.62% increase in the MIoU, demonstrating its strong ability to extract roads from historical maps and provide a valuable reference for road extraction from other types of historical maps.