  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.
