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 …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

Geospatial object segmentation, as a particular semantic segmentation task, always faces
with larger-scale variation, larger intra-class variance of background, and foreground …

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 …

Autonomous navigation requires structured representation of the road network and instance-
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …

Extracting roads from satellite imagery is a promising approach to update the dynamic
changes of road networks efficiently and timely. However, it is challenging due to the …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

Online lane graph construction is a promising but challenging task in autonomous driving.
Previous methods usually model the lane graph at the pixel or piece level, and recover the …

Extracting roads from complex high-resolution remote sensing images to update road
networks has become a recent research focus. How to apply the contextual spatial …