Automatic classification of point clouds for highway documentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F18%3A00326161" target="_blank" >RIV/68407700:21110/18:00326161 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.researchgate.net/publication/326183327_Automatic_classification_of_point_clouds_for_highway_documentation" target="_blank" >https://www.researchgate.net/publication/326183327_Automatic_classification_of_point_clouds_for_highway_documentation</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/AP.2018.58.0165" target="_blank" >10.14311/AP.2018.58.0165</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic classification of point clouds for highway documentation

Popis výsledku v původním jazyce

Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous data sets acquired by these systems is the increase in the demands on the fast and effective processing of these data sets. The crucial part of the roadway data sets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long data set obtained by LYNX Mobile Mapper over a highway.

Název v anglickém jazyce

Automatic classification of point clouds for highway documentation

Popis výsledku anglicky

Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous data sets acquired by these systems is the increase in the demands on the fast and effective processing of these data sets. The crucial part of the roadway data sets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long data set obtained by LYNX Mobile Mapper over a highway.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Polytechnica

ISSN

1805-2363

e-ISSN

1805-2363

Svazek periodika

58

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

165-170

Kód UT WoS článku

000438884700002

EID výsledku v databázi Scopus

2-s2.0-85049504823