This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before. We rigorously investigate both early and late fusion strategies, and assess deep learning based road detection performance using different fusion settings. Our extensive ablation studies assess the efficacy of our framework under diverse model architectures, loss functions, and geographic domains (Istanbul and Montreal). For an unbiased and complete evaluation of road detection results, we use both region-based and boundary-based evaluation metrics for road segmentation. The outcomes reveal that the ResUnet model outperforms U-Net and D-Linknet in road extraction tasks, achieving superior results over the benchmark study using low-resolution Sentinel-2 data. This research not only contributes to the field of automatic road detection but also offers novel insights into the utilization of data fusion methods in diverse applications.

本研究提出了一种创新的深度学习自动道路检测方法，通过融合低分辨率卫星图像和GPS轨迹数据的策略，对早期和后期融合进行深入研究，并使用不同的融合设置评估基于深度学习的道路检测性能。研究结果显示，ResUnet模型在道路提取任务中优于U-Net和D-Linknet，以及使用低分辨率Sentinel-2数据的基准研究，从而为自动道路检测领域作出贡献，并为各种应用中的数据融合方法提供了新的见解。

使用多模态数据的人工智能驱动的道路网络预测