A current flow of interest is to recognize Road Signs. Road Signs are the most essential visual language of the world that represents some special circumstantial information of environment and provides significant information for guiding, warning people to make their movements safer, easier and more convenient. The proposed system introduces a real time Road sign recognition system with a new m...

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficu...

LiDAR is the powerful Remote Sensing Technology for the acquisition of 3D information from terrain surface. This paper surveys the state of the art on automated road feature extraction from airborne Light Detection and Ranging (LiDAR) data. It presents a bibliography of nearly 50 references related to this topic. This includes work related to various main approaches used for extracting road fro...

Road centerline extraction from imagery constitutes a key element in numerous geospatial applications, which has been addressed through a variety of approaches. However, most of the existing methods are not capable of dealing with challenges such as different road shapes, complex scenes, and variable resolutions. This paper presents a novel method for road centerline extraction from imagery in ...

Automatic change detection and data updating is a very important issue for keeping the temporal accuracy and currency of spatial data sets. Road networks are one of the most important parts of geographic database. Firstly two kinds of new algorithms for detecting feature changes that is buffer detection (BD) algorithm and double-buffer detection (DBD) algorithm are illustrated in detail. The co...

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe...

A new approach for object extraction from high-resolution satellite images is presented in this paper. The new approach integrates image fusion, multi-spectral classification, feature extraction and feature segmentation into the object extraction of high-resolution satellite images. Both spectral information from multispectral (MS) images and spatial information from panchromatic (Pan) images a...

In this paper, a robust vehicle local position estimation with the help of single camera sensor and GPS is presented. A modified Inverse Perspective Mapping, illuminant Invariant techniques and object detection based approach is used to localize the vehicle in the road. Vehicles current lane, its position from road boundary and other cars are used to define its local position. For this purpose ...

An automatic road sign detection system localizes road signs from within images captured by an on-board camera of a vehicle, and support the driver to properly ride the vehicle. Most existing algorithms include a preprocessing step, feature extraction and detection step. This paper arranges the methods applied to road sign detection into two groups: general machine learning, neural networks. In...

Automatic road centerline extraction from high-resolution satellite imagery has gained considerable interest recently due to the increasing availability of commercial high-resolution satellite images. In this paper, a hierarchical grouping strategy is proposed to automatically extract main road centerlines from high-resolution satellite imagery. Here hierarchical grouping means that, instead of...