Research on highway area recognition in remote sensing images based on deep learning

Abstract

Accurate highway area recognition is essential for intelligent transportation planning and digital infrastructure management. This paper addresses the challenge of automatic highway extraction from high-resolution remote sensing images, particularly in complex backgrounds, by proposing a semantic segmentation approach based on the DeepLab V3+ model. A high-quality pixel-level annotated dataset was constructed, and model hyperparameters were optimized. Experimental results indicate that DeepLab V3+ achieves strong performance, with an IoU of 87.36% and a Dice coefficient of 86.76%. Visual analysis further confirms robust performance across diverse scenes. The results demonstrate that the proposed method can provide reliable support for intelligent transportation systems and digital infrastructure management.