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

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Extracting road maps from high-resolution optical remote sensing images has received
much attention recently, especially with the rapid development of deep learning methods …

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide
variety of applications, such as autonomous driving, path planning, and road navigation …

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 …

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 …

The acceleration of urbanization and the increasing demand for precise city planning have
made the extraction of buildings and roads from remote sensing images crucial. Deep …

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 …

Object detection has been a fundamental task in the field of remote sensing and has made
considerable progress in recent years. However, the high background complexity in remote …

The data-complete and detail-correct road network information serves as important evidence
in numerous transportation-associated applications. Regular and rapid road network …