AI Powered Road Network Prediction with Multi-Modal Data
This research addresses automatic road detection for urban planning and mapping, but it is incremental as it builds on existing deep learning methods with new data fusion strategies.
The study tackled road detection by fusing satellite imagery and GPS trajectory data, finding that the ResUnet model outperformed others, achieving superior results over benchmarks using low-resolution Sentinel-2 data.
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.