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

Extracting road networks from very-high-resolution (VHR) aerial and satellite imagery has
been a long-standing problem. In this article, a neural-dynamic tracking framework is …

Extraction of road networks in urban areas from remotely sensed imagery plays an important
role in many urban applications (eg road navigation, geometric correction of urban remote …

The binary segmentation of roads in very high resolution (VHR) remote sensing images
(RSIs) has always been a challenging task due to factors such as occlusions (caused by …

This study is aimed at develo** automated methods to recognize railroad infrastructure
from 3D LIDAR data. Railroad infrastructure includes rail tracks, contact cables, catenary …

Updated road network as a crucial part of the transportation database plays an important
role in various applications. Thus, increasing the automation of the road extraction …

In remote sensing analysis, automatic extraction of road network from satellite or aerial
images can be a most needed approach for efficient road database creation, refinement …

Road extraction from very high-resolution (VHR) satellite images plays an important role in
the remote-sensing applications. Although tracing road features in satellite images has …

In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser
Scanning systems are able to capture the 3D environment of the rail tracks with a high level …

ABSTRACT A novel hybrid technique for road extraction from UAV imagery is presented in
this paper. The suggested analysis begins with image segmentation via Trainable Weka …