東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Road Map Inference from GPS Traces: ...

Qiu, Jia.

Linked to FindBook

Google Book

Amazon

博客來

Road Map Inference from GPS Traces: A Segmentation and Grouping Framework.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Road Map Inference from GPS Traces: A Segmentation and Grouping Framework./
Author:	Qiu, Jia.
Description:	109 p.
Notes:	Source: Masters Abstracts International, Volume: 55-03.
Contained By:	Masters Abstracts International55-03(E).
Subject:	Geographic information science and geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1605757
ISBN:	9781339340333

Road Map Inference from GPS Traces: A Segmentation and Grouping Framework.
Qiu, Jia.

Road Map Inference from GPS Traces: A Segmentation and Grouping Framework. - 109 p.

Source: Masters Abstracts International, Volume: 55-03.

Thesis (M.Sc.)--University of Calgary (Canada), 2015.

A road network is one of the most fundamental data of geospatial information. In order to update road maps promptly and consistently, map inference is proposed to automatically generate roads' geometric positions and topological connections from Global Positioning System (GPS) traces. Most of the existing methods are designed to deal with low-noise, densely sampled and uniformly distributed GPS traces. In this research, we propose a novel point clouds segmentation and grouping framework to infer high-quality road maps from high-noise and sparsely sampled GPS traces. First, we segment the points of GPS traces into clusters to represent nearly straight roads. Second, we group the adjacent clusters according to their spatial proximities. Finally, we generate centerlines from the clusters and refine the intersections to form road networks. Experimental results show that our methods are robust to noises and sampling rates. The generated road maps have better geometric accuracy compare to the existing methods.

ISBN: 9781339340333Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

Road Map Inference from GPS Traces: A Segmentation and Grouping Framework.
LDR:01808nmm a2200253 4500 001 2079438
005 20170313112147.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781339340333
035 $a (MiAaPQ)AAI1605757
035 $a AAI1605757
040 $a MiAaPQ $c MiAaPQ
100 1 $a Qiu, Jia. $3 3195118
245 1 0 $a Road Map Inference from GPS Traces: A Segmentation and Grouping Framework.
300 $a 109 p.
500 $a Source: Masters Abstracts International, Volume: 55-03.
502 $a Thesis (M.Sc.)--University of Calgary (Canada), 2015.
520 $a A road network is one of the most fundamental data of geospatial information. In order to update road maps promptly and consistently, map inference is proposed to automatically generate roads' geometric positions and topological connections from Global Positioning System (GPS) traces. Most of the existing methods are designed to deal with low-noise, densely sampled and uniformly distributed GPS traces. In this research, we propose a novel point clouds segmentation and grouping framework to infer high-quality road maps from high-noise and sparsely sampled GPS traces. First, we segment the points of GPS traces into clusters to represent nearly straight roads. Second, we group the adjacent clusters according to their spatial proximities. Finally, we generate centerlines from the clusters and refine the intersections to form road networks. Experimental results show that our methods are robust to noises and sampling rates. The generated road maps have better geometric accuracy compare to the existing methods.
590 $a School code: 0026.
650 4 $a Geographic information science and geodesy. $3 2122917
690 $a 0370
710 2 $a University of Calgary (Canada). $b Geomatics Engineering. $3 3170933
773 0 $t Masters Abstracts International $g 55-03(E).
790 $a 0026
791 $a M.Sc.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1605757