The detailed interpretation of pole-like street furniture in mobile laser scanning data

Research output: Contribution to conferenceAbstractOther research output

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Abstract

The interpretation of pole - like road furniture in mobile laser scanning data has received much attention in recent years. Most current studies interpret road furniture as a single object, which is infeasible for road furniture with multiple classes. In order to tackle this problem, we propose a framework using machine learning classifiers to interpret road furniture into detailed classes based on their functionalities such as street lights and traffic signs connected with poles (Figure 1). The overall accuracy of the interpretation in one test site is higher than 90%. A screenshot of our result is as shown in Figure 2. To conclude, our framework well interprets road furniture at a detailed level, which is of great importance for 3D precise mapping.
Original languageEnglish
Number of pages2
Publication statusPublished - 2018
EventNCG symposium 2018 - Wageningen university, Wageningen, Netherlands
Duration: 29 Nov 201829 Nov 2018
https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Conference

ConferenceNCG symposium 2018
CountryNetherlands
CityWageningen
Period29/11/1829/11/18
Internet address

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laser
road
furniture

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@conference{f0335f8929014bb4906cbb4ee99172be,
title = "The detailed interpretation of pole-like street furniture in mobile laser scanning data",
abstract = "The interpretation of pole - like road furniture in mobile laser scanning data has received much attention in recent years. Most current studies interpret road furniture as a single object, which is infeasible for road furniture with multiple classes. In order to tackle this problem, we propose a framework using machine learning classifiers to interpret road furniture into detailed classes based on their functionalities such as street lights and traffic signs connected with poles (Figure 1). The overall accuracy of the interpretation in one test site is higher than 90{\%}. A screenshot of our result is as shown in Figure 2. To conclude, our framework well interprets road furniture at a detailed level, which is of great importance for 3D precise mapping.",
author = "F. Li and {Oude Elberink}, S.J. and G. Vosselman",
year = "2018",
language = "English",
note = "NCG symposium 2018 ; Conference date: 29-11-2018 Through 29-11-2018",
url = "https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018",

}

Li, F, Oude Elberink, SJ & Vosselman, G 2018, 'The detailed interpretation of pole-like street furniture in mobile laser scanning data' NCG symposium 2018 , Wageningen, Netherlands, 29/11/18 - 29/11/18, .

The detailed interpretation of pole-like street furniture in mobile laser scanning data. / Li, F.; Oude Elberink, S.J.; Vosselman, G.

2018. Abstract from NCG symposium 2018 , Wageningen, Netherlands.

Research output: Contribution to conferenceAbstractOther research output

TY - CONF

T1 - The detailed interpretation of pole-like street furniture in mobile laser scanning data

AU - Li, F.

AU - Oude Elberink, S.J.

AU - Vosselman, G.

PY - 2018

Y1 - 2018

N2 - The interpretation of pole - like road furniture in mobile laser scanning data has received much attention in recent years. Most current studies interpret road furniture as a single object, which is infeasible for road furniture with multiple classes. In order to tackle this problem, we propose a framework using machine learning classifiers to interpret road furniture into detailed classes based on their functionalities such as street lights and traffic signs connected with poles (Figure 1). The overall accuracy of the interpretation in one test site is higher than 90%. A screenshot of our result is as shown in Figure 2. To conclude, our framework well interprets road furniture at a detailed level, which is of great importance for 3D precise mapping.

AB - The interpretation of pole - like road furniture in mobile laser scanning data has received much attention in recent years. Most current studies interpret road furniture as a single object, which is infeasible for road furniture with multiple classes. In order to tackle this problem, we propose a framework using machine learning classifiers to interpret road furniture into detailed classes based on their functionalities such as street lights and traffic signs connected with poles (Figure 1). The overall accuracy of the interpretation in one test site is higher than 90%. A screenshot of our result is as shown in Figure 2. To conclude, our framework well interprets road furniture at a detailed level, which is of great importance for 3D precise mapping.

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2018/pres/li_det_abs.pdf

M3 - Abstract

ER -