Results 161 to 170 of about 26,498 (203)

Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression

IEEE Geoscience and Remote Sensing Letters, 2016
Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality.
Guangliang Cheng, Feiyun Zhu, Shiming Xiang, Chunhong Pan   +3 more
openaire   +3 more sources

A Semi-Supervised High-Level Feature Selection Framework for Road Centerline Extraction

IEEE Geoscience and Remote Sensing Letters, 2020
Accurate road centerline extraction is very important for many vital applications. In the road extraction, the acquisition of labeled data is time-consuming; thus, there is only a small amount of labeled samples in reality. To solve the problem of limited labeled samples, a semi-supervised road centerline extraction is proposed, which incorporates high-
Ruyi Liu, Qiguang Miao, Yi Zhang, Maoguo Gong, Pengfei Xu   +4 more
openaire   +3 more sources

Cascaded Multi-Task Road Extraction Network for Road Surface, Centerline, and Edge Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2022
Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, Dingyuan Chen, Yu Su, Ailong Ma, Liangpei Zhang   +6 more
openaire   +3 more sources

Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net

IEEE Transactions on Geoscience and Remote Sensing, 2019
Road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods
Xiaofei Yang, Xutao Li, Yunming Ye, Raymond Y. K. Lau, Xiaofeng Zhang, Xiaohui Huang   +5 more
openaire   +3 more sources

Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization

ISPRS Journal of Photogrammetry and Remote Sensing, 2016
Abstract Road information acquisition is an important part of city informatization construction. Airborne LiDAR provides a new means of acquiring road information. However, the existing road extraction methods using LiDAR point clouds always decide the road intensity threshold based on experience, which cannot obtain the optimal threshold to extract ...
Zhenyang Hui, Youjian Hu, Shuanggen Jin, Yao Ziggah Yevenyo   +3 more
openaire   +3 more sources

An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2014
Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps.
Wenzhong Shi, Zelang Miao, Johan Debayle
openaire   +3 more sources

Modeling Road Centerlines and Predicting Lengths in 3‐D Using LIDAR Point Cloud and Planimetric Road Centerline Data

Computer-Aided Civil and Infrastructure Engineering, 2008
Abstract:  Transportation is one of a few engineering domains that work with linear objects—roads. Accurate road length information is critical to numerous transportation applications. Road lengths can be obtained via technologies such as ground surveying, global positioning systems (GPS), and Distance Measurement Instruments (DMI).
Hubo Cai, William Rasdorf
openaire   +1 more source