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 in very-high-resolution (VHR) aerial images is one of the most
challenging tasks in remote sensing image understanding. Most of the current approaches …

Semantic segmentation plays an important role in remote sensing image understanding.
Great progress has been made in this area with the development of Deep Convolutional …

Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …

Automatic road extraction from remote sensing images plays an important role for
navigation, intelligent transportation, and road network update, etc. Convolutional neural …

Recent advances in deep-learning methods have shown extraordinary performance in road
extraction from high resolution satellite imagery. However, most existing deep-learning …

Deep neural networks perform well in road extraction from very high-resolution satellite
imagery. A network with certain reasoning ability will give more satisfactory road network …

The use of very-high-resolution images to extract urban, suburban and rural roads has
important application value. However, it is still a problem to effectively extract the road area …

Road extraction from optical remote sensing images has many important application
scenarios, such as navigation, automatic driving and road network planning, etc. Current …