High-Quality Road Detection Using U-Net-Based Semantic Segmentation with High-Resolution Orthophotos and DSM Data in Urban Environments

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Integrating digital surface models with orthophotos achieves a road detection accuracy of 95.95%.
The study highlights improvements in road detection metrics, surpassing previous methods that used only imagery.
Utilizing U-Net-based semantic segmentation enables precise delineation of urban road networks effectively.
This approach underlines the significance of combining different data sources for enhancing road detection frameworks.

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Analysis reveals improved road detection accuracy in urban environments, suggesting multimodal data benefits.

Fawzy et al. (2025) studied this question.

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