The road network graph is a critical component for downstream tasks in autonomous driving,
such as global route planning and navigation. In the past years, road network graphs are …

Road network graphs provide critical information for autonomous-vehicle applications, such
as drivable areas that can be used for motion planning algorithms. To find road network …

Graph-based approaches have been becoming increasingly popular in road network
extraction, in addition to segmentation-based methods. Road networks are represented as …

We propose SAM-Road an adaptation of the Segment Anything Model (SAM) for extracting
large-scale vectorized road network graphs from satellite imagery. To predict graph …

Extracting road graphs from aerial images automatically is more efficient and costs less than
from field acquisition. This can be done by a post-processing step that vectorizes road …

Road graph extraction from remote sensing images aims at extracting topological maps
composed of road vertices and edges, which has broad prospects in urban planning, traffic …

Topological road-boundary detection using remote sensing imagery plays a critical role in
creating high-definition (HD) maps and enabling autonomous driving. Previous approaches …

Automatic road graph extraction from aerial and satellite images is a long-standing
challenge. Existing algorithms are either based on pixel-level segmentation followed by …

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 …

With the fast development of autonomous driving technologies, there is an increasing
demand for high-definition (HD) maps, which provide reliable and robust prior information …