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The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this researc...
Ground-penetrating 2D radar scans are captured in road environments for examination of pavement condition and below-ground variations such as lowerings and developing pot-holes. 3D point clouds captur...
LIDAR is a powerful remote sensing technology for acquisition of 3D information from terrain surface. Algorithms used for LIDAR data in urban area are used mostly to deal with the 3D points cloud an...
This paper describes the first phase of a three year PhD research project which aims at extracting road and roadside information accurately and robustly from airborne sensor data and assessing its sui...
LiDAR technology, the primary source of highly accurate surface data at large scale, has seen remarkable developments in recent years. Specifically, the accuracy of the laser ranging has reached the f...
Automated and reliable 3D city model acquisition is an increasing demand. Automatic road extraction from dense urban areas is a challenging issue due to the high complex image scene. From imagery, t...
This paper deals with automatic road extraction from SAR imagery. In general, automatically extracted road networks are not complete, i.e., gaps remain in the erxtracted network. Especially in SAR im...
Automatic extraction of roads is a present research topic. Many applications like topographic mapping, navigation applications, or image registration could profit from such algorithms.
Growing demand for an efficient land use above and below the ground is motivating cadastre and land management systems to move from traditional 2D systems toward three dimensional ones. Airborne laser...
In view of the fact that road network detection effect in high-resolution image is not satisfying, an approach for road network detection based on Bayesian Network is put forward in this paper. First,...
Detection of Text on Road Signs from Video     Road Signs  Video       2010/12/15
A fast and robust framework for incrementally detecting text on road signs from video is presented in this paper.This new framework makes two main contributions. 1) The framework applies a divide-and-...
This paper proposes a fast and robust framework for incrementally detecting text on road signs from natural scene video. The new framework makes two main contributions. First, the framework applies a...
The data provided by LIDAR systems are presented in a dense, accurate three-dimensional form without point classification, such as buildings, trees roads, terrain, etc. The principal objective of th...

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