Investigating the impact of Sentinel-2 image super-resolution on urban road detection in Dubai

Abstract

Road segmentation from satellite imagery is critical for urban planning and transportation analysis, but is often limited by the low spatial resolution of publicly available data and the high cost of high-resolution alternatives. This study evaluates the impact of super-resolution (SR) on urban road network extraction in Dubai. Sentinel-2 (S2) imagery with a native resolution of 10m was enhanced to approximately 1m using the Sentinel-2 Deep Resolution 3 (S2DR3) model, and road segmentation was performed using the SAM-LoRA model. The model was trained on 105 km2 of Dubai city using a small set of manually annotated road segments and evaluated on an independent region. Despite limited training annotations, the model achieved robust performance, demonstrating the effectiveness of combining SR preprocessing with the SAM-LoRA architecture. Due to the lack of high-resolution reference imagery, no-reference image quality metrics were used to assess SR output. Quantitative results indicate that SR improves segmentation performance, achieving an overall accuracy of 0.91, Kappa of 0.81, IoU of 0.82, and F1-score of 0.90, compared to 0.68, 0.35, 0.36, and 0.54, respectively, for the original S2 imagery. Qualitative analysis shows that SR generates smooth, continuous road networks, whereas outputs from the original S2 imagery were fragmented and imprecise. Beyond the testing region, the SR imagery and trained model were applied to the entire city of Dubai (∼ 1700 km2), producing a city-wide road network and demonstrating strong generalization. These findings confirm that SR preprocessing can improve the accuracy of extracting urban road networks from low-resolution satellite images.