Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

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 …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …

Road information from high-resolution remote-sensing images is widely used in various
fields, and deep-learning-based methods have effectively shown high road-extraction …

Road segmentation from remote-sensing images is a challenging task with wide ranges of
application potentials. Deep neural networks have advanced this field by leveraging the …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Road extraction from remote sensing imagery is a popular and frontier research focus, since
road information plays an essential role in application fields, such as urban management …

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 …

Pavement defect detection is of profound significance regarding road safety, so it has been a
trending research topic. In the past years, deep learning based methods have turned into a …

Road detection technology plays an essential role in a variety of applications, such as urban
planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there …