Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …

Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …

Road surface extraction from remote sensing images using deep learning methods has
achieved good performance, while most of the existing methods are based on fully …

Inferring road graphs from satellite imagery is a challenging computer vision task. Prior
solutions fall into two categories:(1) pixel-wise segmentation-based approaches, which …

Automatic extraction of the road surface and road centerline from very high-resolution (VHR)
remote sensing images has always been a challenging task in the field of feature extraction …

Mountain roads are a source of important basic geographic data used in various fields. The
automatic extraction of road images through high-resolution remote sensing imagery using …

Road network extraction is one of the significant assignments for disaster emergency
response, intelligent transportation systems, and real-time updating road network. Road …

Semantic segmentation is an important and challenging task in the aerial image community
since it can extract the target level information for understanding the aerial image. As a …

Road network extraction from remotely sensed imagery has become a powerful tool for
updating geospatial databases, owing to the success of convolutional neural network (CNN) …

The up-to-date and information-accurate road database plays a significant role in many
applications. Recently, with the improvement in image resolutions and quality, remote …