Road Extraction

Road extraction, the automated process of identifying and mapping roads from various data sources like satellite imagery and historical maps, aims to improve efficiency and accuracy in tasks such as urban planning and infrastructure management. Current research emphasizes deep learning approaches, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), often incorporating techniques like multi-task learning, contrastive learning, and data augmentation to enhance performance and address challenges such as incomplete data and complex road geometries. These advancements are significantly impacting various fields, enabling more efficient road network monitoring, improved infrastructure assessment, and facilitating socioeconomic analysis in underserved areas.